Advances in Seed Health Testing: Integrating Molecular Diagnostics, Imaging, and AI for Enhanced Quality Assurance

Abstract

Seed health testing is a cornerstone of global food security, yet traditional diagnostic workflows often struggle to balance speed, sensitivity, and regulatory confidence under low-prevalence and heterogeneous seed lot conditions. This review synthesizes recent advances in molecular diagnostics (PCR, qPCR, LAMP, and digital PCR), non-destructive imaging technologies (hyperspectral, X-ray, and thermal imaging), and data-driven analytical approaches for pathogen detection in seeds. Emphasis is placed on the practical integration of these tools within high-throughput, ISO/IEC 17025-compliant laboratory workflows, highlighting their respective strengths, limitations, and roles in risk-based decision-making. Comparative discussions address cost, sensitivity, turnaround time, and field deployability across diagnostic platforms, supported by crop- and pathogen-specific examples. Emerging approaches such as CRISPR-based biosensing, advanced sequencing, and imaging-assisted analytics are discussed in the context of validation, regulatory acceptance, and operational feasibility. By focusing on implementation rather than conceptual frameworks, this review provides a pragmatic reference for laboratories, regulators, and seed companies seeking to modernize seed health testing while safeguarding trade integrity and biosecurity.

1. Introduction

Seeds play a vital role in plant survival, enabling species to persist through unfavorable conditions, disperse across environments and protect embryos from stresses such as desiccation [1]. Seed security is an essential yet often overlooked component of global food security [2]. As the foundation of agriculture, seeds serve as the primary reproductive unit of plants and the vehicle for delivering genetic potential that supports crop productivity, diversity and resilience in changing environments [3]. The use of high-quality seeds ensures uniform germination, vigorous seedling growth and higher yields, whereas poor-quality or contaminated seeds can result in weak establishment and significant yield losses [4]. Moreover, seed health, encompassing purity, germination capacity and absence of pathogens and pests, directly influences crop performance and long-term agricultural sustainability [5].

Seed health testing continues to play a critical role, even with rapid advances in diagnostics and seed technology. The international trade of seeds has increased substantially with globalization, expanding both commercial opportunities and phytosanitary risks, which underscores the continuing importance of effective seed health testing [6]. A single contaminated lot may introduce exotic pathogens into new regions, potentially disrupting production or triggering regulatory action, consistent with known risks of contaminants in traded commodities [7]. Seedborne pathogens can significantly reduce crop yields and seed quality, leading to economic losses for growers and potential disruptions in market supply chains [8,9]. Beyond these direct impacts, ensuring seed health is also a crucial component of food safety, as it mitigates the introduction of toxin-producing and human-pathogenic microorganisms into the food supply. The implementation of rigorous seed sanitation and treatment protocols reduces the dependence on post-emergence disease management practices, contributing to improved crop health and minimizing the risk of contaminant introduction into the food production system [10].

Because seed health affects many aspects of crop production and trade, it is important to clearly define what it entails. In this context, the term refers to the overall physiological and pathological condition of the seed, encompassing both its physical quality and whether it is free from infectious agents [11]. Within this framework, seedborne, seed-associated, and surface contamination are distinct but related terms. Seedborne pathogens are internally present within the seed tissues and can be transmitted to the seedling [12,13]. Seed-associated microorganisms may occur on or within the seed without necessarily being transmitted or causing disease [14]. Surface contamination refers to the presence of pathogens adhering to the outer seed surface, which can often be managed through disinfestation or seed treatment. Seed disinfestation targets spores and other propagules on the seed surface, whereas disinfection aims to eliminate pathogens that have penetrated the internal seed tissues

Despite improvements in technology, several challenges continue to limit seed health testing. Testing seeds for plant pathogens can be challenging, as infested seeds are often asymptomatic, pathogen populations occur at very low levels, and contamination may be unevenly distributed within a seed lot. These factors make detection difficult and may require large sample sizes or highly sensitive assays to achieve reliable results [15]. The seed industry ensures the delivery of healthy seeds while complying with national and international phytosanitary regulations. Seed health testing combines direct methods, which confirm pathogen viability and pathogenicity, with indirect methods such as Enzyme-Linked Immunosorbent Assay (ELISA), Polymerase Chain Reaction (PCR), or high-throughput sequencing for rapid prescreening [11]. While molecular assays are sensitive, they may detect noninfectious fragments and require confirmation by classical tests [12]. The integration of both approaches allows accurate risk assessment, supporting safe international seed movement and global food security [16,17].

This review highlights recent developments and ongoing challenges in detecting and managing seedborne fungi, bacteria, and viruses/viroids, with brief notes on oomycetes and nematodes that can occasionally be associated with seed movement. Emphasis is given on diagnostic advances, harmonization efforts, and regulatory perspectives, illustrating why seed health testing remains indispensable in the modern era of global agriculture. This review underscores that achieving robust seed health assurance in global agriculture depends on integrating technological innovation with harmonized regulatory standards, validated diagnostic methods, and equitable resource accessibility, while maintaining attention to biological and operational constraints. In essence, success requires aligning cutting-edge technologies with regulatory frameworks and practical implementation capacity.

2. Regulatory and Standards Landscape

Advances in molecular diagnostics, high-resolution imaging, and emerging artificial intelligence (AI)-assisted detection tools are increasingly shaping seed health testing, making regulatory harmonization essential to ensure that new technologies are validated, recognized, and consistently applied across trading systems [11,12]. The regulation of seed health is supported by a network of international, regional, and national organizations that develop testing standards, diagnostic protocols, and certification systems. Together, these frameworks ensure that seeds moving through domestic and international trade channels are tested, certified, and managed consistently to minimize phytosanitary risk while enabling market access. At the global level, several key bodies play distinct yet complementary roles. The International Seed Testing Association (ISTA) establishes standardized seed testing methods, including molecular assays that are increasingly incorporated into routine diagnostics, and issues certificates widely recognized in international trade [18]. In the United States, the Association of Official Seed Analysts (AOSA) and the Society of Commercial Seed Technologists (SCST) maintain and standardize seed testing standards for both regulatory and commercial purposes, with ongoing discussions regarding integration of qPCR, LAMP, and other molecular technologies into official rulebooks [19]. Within Europe, the European and Mediterranean Plant Protection Organization (EPPO) develops diagnostic protocols and plant health standards, including detailed molecular and serological methods for detecting seedborne pathogens [20]. For the vegetable seed sector, the International Seed Health Initiative for Vegetable Crops (ISHI Veg) focuses on standardized diagnostic methods and validation data and is currently evaluating next-generation sequencing (NGS) approaches and digital imaging tools to establish pathways for future standardization [18]. Broader phytosanitary oversight is provided by the International Plant Protection Convention (IPPC), which develops International Standards for Phytosanitary Measures (ISPMs) that guide how countries regulate the movement of plants and seeds to prevent the spread of quarantine pests [21,22]. Complementing these, the OECD Seed Schemes provide an internationally recognized framework for varietal certification and facilitate movement of seed among participating countries under standardized labeling and quality systems [23].

Accreditation and validation form the technical foundation that ensures confidence in test results and comparability between laboratories [20]. Many seed testing laboratories operate under ISO/IEC 17025 accreditation, which establishes requirements for technical competence, quality management, and method reliability [24]. New diagnostic methods, particularly qPCR, LAMP, NGS, high-throughput imaging, and emerging AI-assisted classification tools, undergo formal validation pathways that assess parameters such as analytical sensitivity, specificity, repeatability, and reproducibility [25]. Collaborative ring trials and proficiency testing are essential components of this process, allowing multiple laboratories to evaluate the same method under comparable conditions [26]. These exercises help identify variability, refine protocols, and demonstrate that a test can perform reliably across different operators and environments, ultimately supporting broader acceptance by regulators and trading partners [27]. However, while validation frameworks for molecular assays are well established, comparable regulatory pathways for AI-based image analysis and digital diagnostics are still under development, creating uncertainty around how such tools will be incorporated into official standards.

Despite these advances, achieving full standardization across laboratories and regulatory systems remains challenging. Discrepancies persist in the choice of target pathogens, sample sizes and sampling strategies, and acceptable thresholds for detection or tolerance [28]. Some standards emphasize presence or absence criteria, while others set quantitative thresholds based on inoculum levels or disease risk [29]. Rapid molecular tests may detect non-viable or residual pathogen DNA, raising questions about the biological relevance of positive results. The introduction of high-throughput sequencing and AI-assisted imaging further complicates interpretation, as these technologies can generate highly sensitive outputs whose regulatory significance is not yet fully defined. Additionally, differences in regulatory interpretation and resource capacity among countries contribute to inconsistent implementation. Because of these differences, a seed lot that meets the standards in one system may not comply with another, causing uncertainty for exporters, labs, and regulators.

The ongoing evolution of the seed trade, combined with rapid innovation in diagnostic technologies, underscores the need for greater international coordination. Building consensus on validation frameworks, data sharing, performance criteria, and risk-based decision-making will be key to aligning scientific advances, particularly molecular, digital, and AI-assisted tools, with regulatory trust [30]. Only through such standardization can the global seed industry maintain both the efficiency of commerce and the integrity of plant health protection.

3. Sampling Theory and Study Design

Accurate detection of pathogens in seed lots depends on well-designed sampling strategies. Because seed lots are large and contamination is often rare and uneven, traditional sampling rules are not always sufficient. This section introduces modern approaches such as risk-based and Bayesian designs that improve detection while managing cost and uncertainty. It also covers pooling methods, models for heterogeneity, and operational factors like sample handling and transport. Together, these elements support reliable, efficient, and scientifically grounded sampling plans.

3.1. Lot Size and Statistical Sampling Plans

Seed lots often contain extremely large numbers of individual units, and pathogen presence is typically rare and nonuniform. The classical “square-root rule” (e.g., sample size ∝ √N, where N = lot size) can sometimes serve as a rough heuristic [31] but does not account for heterogeneity or prior information. In modern frameworks, two important enhancements are:

• Risk-based sampling designs: These allocate more sampling effort to lots with higher prior risk (due to origin, field history, or previous surveillance results). The idea is that not all lots are equal in their prior probability of contamination.
• Bayesian sampling designs: These explicitly incorporate prior probabilities of infection (or contamination) and allow calculation of posterior probability of presence/absence after sampling [32].

In a Bayesian framework, one might specify a prior distribution on the proportion of infected seeds in the lot (e.g., beta distribution) and then update that with observed negatives/positives to compute a posterior credible interval for the infection prevalence or a posterior probability of “freedom from infection.” This is often more informative than frequentist fixed-size designs when pathogen prevalence is very low [33].

When designing a sampling plan, one can calculate, for any proposed sample size n, the probability of detecting at least one infected seed given a true underlying prevalence p, using:

P(detect at least one infected seed) =1 − (1 − p)ⁿ

where:

• p = true infection prevalence (probability that a seed is infected);
• n = number of seeds sampled.

Assumptions:

• Independent sampling;
• Homogeneous infection distribution (for clustered infection, use beta-binomial or negative binomial models).

For example:

• If $p=0.001$ (0.1%) and $n=3000$, then:

$$P=1-(1-0.001{)}^{3000}\approx 0.95$$

This means a 95% chance of detecting at least one infected seed.

One also often defines a required confidence level (e.g., 95% or 99%) that the lot is “free” of infection at or below a threshold prevalence (the “damage threshold”) if no positives are found. Ref. [34] outlines this in the context of seed health testing. In regulatory settings, such probabilistic designs support transparent decision thresholds (i.e., lot acceptance or rejection) and can be aligned with phytosanitary risk tolerance.

3.2. Composite/Pooling Strategies

Pooling (or compositing) is a practical strategy to reduce the number of assays needed, by mixing subsamples from multiple units (or seeds) and testing them together. If a pooled test is negative, one infers that none of the constituent units are positive; if positive, further deconvolution or individual testing may follow.

Key factors in pooling design:

• Pool size dilution effect: if the pathogen load per infected seed is low, pooling too many seeds may dilute the target nucleic acid below the assay’s limit of detection (LOD).
• Homogenization and mixing uniformity: to reduce sampling bias, the pool must be well mixed so that each aliquot is representative.
• Adaptive or algorithmic pooling: modern approaches use Bayesian or information-theoretic optimization (e.g., maximizing mutual information) to define which pools to test, how large, and how to split in follow-ups [35].
• D-optimal pooling design: for a known prior infection probability and test error rates, one can optimize pool groupings to maximize the information gained.

Regulatory frameworks such as ISTA and EPPO set specific limits on pooling size for official testing. These limits are designed to ensure assay sensitivity and minimize false negatives due to dilution. Compliance with such standards is essential for results to be accepted in international trade and phytosanitary certification.

Thus, pooling strategies must balance cost efficiencies against increased false negative risk due to dilution or sample heterogeneity.

3.3. Heterogeneity Models and Implications for Detection Limits

Real seed lots frequently exhibit aggregation or clustering of infected seeds rather than a uniform random distribution. A simple binomial model (constant p) often underestimates variability. Some of the alternative models are:

• Negative binomial distribution: To model over-dispersed counts of infected seeds (i.e., variance > mean). Under this model one can derive the probability that a sample of size n will include at least one infected seed, accounting for clustering.
• Hierarchical occupancy–detectability models: For example, when each sub-unit (e.g., packet, bag, sublot) has its own probability of being infected, and detection within is probabilistic [35].
• Beta-binomial model: The beta-binomial model assumes that infection probability varies across subsamples according to an underlying beta distribution, capturing extra-binomial variation and spatial heterogeneity in seedborne inoculum. This approach better reflects real-world seed lot structure and improves estimation of detection probability, especially when contamination is patchy [36]. These models allow more realistic estimation of false negative risks at fixed sample sizes. For example, even when an assay’s LOD is one infected seed per 1000, clustering in only a few sublots may cause non-detection if sampling is not spatially stratified [37].

In practice, heterogeneity modeling often draws on spatial sampling datasets, outbreak maps, or controlled spiking studies to estimate clustering parameters [38].

3.4. Chain of Custody, Transport, Storage, and Pre-Analytical Variables

Maintaining a rigorous chain of custody from sampling through analysis is essential for data integrity under ISO/IEC 17025 frameworks [39]. Each subsample should be barcoded or labeled with identifiers, timestamps, and handling records to ensure full traceability [40]. Transport and storage conditions strongly influence pathogen viability and nucleic acid stability. Temperature, humidity, and physical agitation can degrade nucleic acids or suppress viability; for molecular workflows, seeds or extracts are typically stored at ≤4 °C or frozen with desiccants to reduce hydrolysis and oxidation [41,42]. Seed coatings, fungicides, and other surface treatments can inhibit PCR or other downstream assays, making standardized wash steps or inhibitor-removal protocols necessary [43]. Homogenization before subsampling reduces bias caused by segregation of seeds by size, density, or damage [44]. Temporal decay in viability or RNA integrity during storage is also well documented; stability studies are often required to understand degradation rates and their effect on detection [42].

3.5. Integration with Diagnostic Workflows

Digitalization increasingly supports pre-analytical quality: automated sample tracking, sensor-based environmental monitoring, and anomaly detection improve auditability and help maintain sample integrity. Linking metadata to assay results via LIMS platforms enhances retrospective risk modeling and adaptive sampling [39]. Poor control of pre-analytical conditions can inflate false negative rates and violate assumptions of the statistical sampling model. This supports retrospective evaluation of sampling effectiveness, recalibration of risk models, and estimation of residual risk for lots testing negative.

Adaptive sampling frameworks allow modification of sampling intensity as results accumulate; early negatives may reduce sampling depth, whereas borderline or near-threshold results may trigger additional subsamples. Bayesian updating provides a formal structure for recalculating posterior risk and adjusting decision thresholds accordingly [36].

Integration of sampling and diagnostic databases enables advanced analytics. By aggregating historical sampling patterns, qPCR or Ct-value distributions, and contextual metadata, predictive models can prioritize high-risk lots, identify systemic vulnerabilities, and optimize resource allocation

4. Conventional Diagnostics (Baseline)

4.1. Overview

Conventional seed health diagnostics form the foundation of seed sanitary quality assessment, providing essential information on seed viability and the presence of seedborne pathogens, prior to advanced molecular, imaging, or AI-enabled approaches. The established approaches such as blotter tests, agar plate incubations, grow-out procedures, and seedling assays, ELISA and lateral flow immunoassays (LFIA) remain integral to seed health certification. A critical appraisal of these traditional methods is presented with particular emphasis on key performance dimensions such as the readout of viability, time-to-result, and operator dependence that influence decision-making in seed health management and phytosanitary certification [45,46]. While conventional methods offer direct viability readouts and relatively cost-effective implementation, they may entail longer turnaround times and require trained personnel, considerations which motivate the ongoing development and integration of rapid adjuncts and automated readouts in contemporary seed health programs [47]. The conventional diagnostics put traditional seed health assays as indispensable anchors for quality assurance, while clarifying their limitations and the value of synergistic integration with modern diagnostic modalities [48].

4.2. Blotter, Agar Plate, Grow-Out, Incubation and Seedling Assays

Traditional seed health diagnostics rely on a variety of culture-based methods that assess seed viability and detect seedborne pathogens. These methods include blotter tests, agar plate and grow-out assays, incubation followed by seedling evaluations, and related seedling assays. Together, these tests provide rapid screening, culturable pathogen recovery, and functional readouts of seed health. When complemented with immunoassays and newer rapid diagnostics, these conventional methods remain foundational for seed health certification, regulatory screening, and quality assurance programs [47].

Across the literature, these methods are described as rapid, cost-effective, and standardized (e.g., ISTA guidelines). However, they do depend on culturable organisms, can be subjective in interpretation, and may entail variable turnaround times for pathogen identification and culture recovery. These themes are consistently highlighted in contemporary reviews and primary studies, especially in relation to their roles in baseline diagnostics, sanitary status assessment, and guiding sanitation interventions [49,50].

The relationship between blotter tests, agar plate/grow-out assays, incubation, and seedling assays can be understood as a diagnostic continuum. Blotter tests and seedling-based germination assays provide rapid, functional insights into seed health. These tests help identify pathogens and evaluate seed vigor, revealing issues like abnormal seedling development or germination failure. Standard vigor assessments, which often use early germination counts, serve as proxies for seed lot vigor, acknowledging that seed deterioration slows germination and reduces vigor indices [51]. Blotter tests, while qualitative in nature, can be supported by laboratory confirmation for pathogen identity, making them an effective first-pass filter before more resource-intensive assays [52].

Agar plate assays involve placing seeds on nutrient media to promote the growth of seedborne fungi and bacteria. This method enables direct observation of pathogen colonies and facilitates identification through morphological traits. While highly informative for culturable organisms, agar plate assays require sterile conditions and skilled interpretation, making them more suitable for laboratory settings than field use. These methods align with ISTA guidelines and are integral to health surveillance programs for multiple crops [53,54]. When needed, they also support pathogenicity testing for quarantine and certification purposes. However, these approaches rely on culturable organisms, meaning that non-culturable or slow-growing pathogens might be overlooked, potentially underestimating disease burden [47].

Grow-out assays focus on germinating seeds under controlled conditions to observe disease symptoms in seedlings. This approach provides functional evidence of pathogen transmission and its impact on seedling vigor. Although grow-out assays are time-intensive (often 14–30 days), they remain essential for confirming pathogenicity and assessing the practical risk of infection. Seedling-based metrics offer valuable insights into how infections affect establishment and yield, particularly for crops where seedling vigor directly correlates with field performance [55]. Like blotter tests, seedling assays can be influenced by environmental factors and observer interpretation. Thus, standardizing conditions and scoring is crucial to ensure comparability across laboratories and programs [56,57].

Incubation assays involve exposing seeds to specific environmental conditions (temperature, humidity) that favor pathogen expression before germination. These tests help detect latent infections that may not be visible under standard conditions. Incubation assays are often combined with seedling evaluations to provide a comprehensive picture of seed health, though they require precise environmental control [55,56].

Synthesis: Collectively, these methods form a diagnostic spectrum that begins with rapid viability screening (blotter and seedling assays), progresses to pathogen recovery and characterization (agar plate and grow-out assays), and culminates in functional assessments of disease risk (incubation and seedling tests). The use of these traditional methods provides practical and standardized tools for seed health certification, screening during production, and regulatory programs. While they are foundational, the reliance on culturable organisms and potential subjectivity in observations highlights the need for integration with molecular, immunoassay, and imaging-based approaches. By incorporating these advanced technologies, traditional diagnostics can be enhanced, forming a more cohesive, multi-modal framework for seed health management. This integrated approach aligns with the growing trend to couple conventional methods with rapid, automated diagnostics to improve seed health programs and decision-making [58].

4.3. ELISA and Lateral Flow for Priority Pathogens

4.3.1. ELISA and Seed Health Applications

ELISA remains a core high-throughput method for screening seed lots for priority seedborne pathogens, especially viruses and, to a lesser extent, bacteria and fungi [49]. It detects pathogen antigens, such as viral coat proteins or bacterial/fungal determinants through antibody–antigen binding on microplates with enzyme-linked colorimetric, fluorescent, or chemiluminescent detection. Double Antibody Sandwich (DAS)-ELISA, indirect ELISA, and competitive ELISA offer different balances of sensitivity, antibody requirements, and ease of standardization [59]. Reliable performance depends heavily on optimized extraction because seed matrices rich in oils, polyphenols, and storage proteins can inhibit antigen capture; ISTA protocols emphasize bulked seed extracts, homogenization, clarification, and spiking approaches for validation. ELISA remains widely used for seedborne viruses such as potyviruses and tobamoviruses, and commercial kits support routine screening in certification, quarantine, and surveillance programs [60]. Although adaptations exist for bacterial pathogens (e.g., Xanthomonas spp.) and some fungal antigens, their deployment is more limited due to challenges in antibody development and antigen extraction [61,62].

4.3.2. LFIA Principles, Advantages, and Performance Considerations

Lateral flow immunoassays apply the same antibody–antigen recognition to membrane strips, generating visible test lines as extracts migrate by capillarity. Sandwich and competitive designs enable rapid, field-ready detection within minutes, and recent advances integrate nanoparticles, engineered reporters, and portable readers for improved sensitivity and quantitation [63]. LFIA is increasingly used for on-site screening of seedborne viruses and select bacterial/fungal targets, though careful validation remains essential [64]. Compared with nucleic acid assays, both ELISA and LFIA offer speed and low cost but generally lower analytical sensitivity than PCR/qPCR/RT-qPCR; molecular assays detect lower titers while immunoassays may tolerate some inhibitors yet risk cross-reactivity [64,65]. Because immunoassays detect antigens irrespective of viability, positive results require confirmation through culture, grow-out, or PCR for regulatory decisions [65]. Best practice, therefore, positions ELISA/LFIA as front-end screens within integrated workflows endorsed by ISTA and proficiency-testing programs [66], with strong user acceptance reported for LFIA in on-farm and quarantine contexts [67]. The main conventional diagnostic methods are blotter tests, agar plate incubation, grow-out procedures, and immunoassays.

4.4. Strengths/Limitations: Viability Readout, Time-to-Result, Operator Dependence

A key limitation shared by most conventional diagnostic assays—including blotter, agar plate, incubation, grow-out, and seedling tests, as well as immunoassays such as ELISA and LFIAs—is their variable capacity to infer pathogen viability. Culture-based methods (e.g., agar plate and blotter assays) provide a direct indication of viability, since detection depends on the successful germination, growth, and sporulation of the pathogen from the seed; for this reason, they remain the gold standard for viability confirmation and are routinely required for regulatory seed health certification. In contrast, immunoassays detect antigenic proteins or epitopes that may persist even in non-viable or inactivated cells, meaning a positive ELISA or LFIA result verifies the presence of pathogen-derived antigenic material but not infectivity or transmission potential [68]. This limitation poses challenges for quarantine and phytosanitary decisions, where confirmation of “live” pathogens is critical. The need to discriminate between viable and non-viable propagules has therefore stimulated the development of viability PCR (vPCR) and other advanced nucleic acid-based techniques that incorporate intercalating dyes (e.g., PMA or EMA) to selectively suppress amplification of DNA from dead cells, thereby improving the biological relevance of molecular detection, although these remain outside the scope of most routine or conventional seed diagnostic workflows [69].

Immunoassays such as ELISA and LFIA provide rapid turnaround (ELISA in a few hours, LFIA in minutes) combined with a relatively low cost per sample when assays are batched, making them attractive for moderately high-throughput screening (e.g., as in seed health testing protocols). LFIA requires minimal laboratory equipment and modest operator training, which enables field deployment or use in resource-limited settings. Where well-characterized antibodies and standardized extraction protocols are used, the assays often yield good reproducibility; and ELISA formats lend themselves to automation (e.g., via plate washers and microplate readers) to further scale throughput. Because immunoassays detect antigenic molecules and not viability, positive detections do not guarantee that the pathogen is alive or infectious, thus confirmation via culture, bioassay, or grow-out may be required to satisfy regulatory phytosanitary or seed health endpoints (as noted in seed health best practices) [70]. The sensitivity of ELISA and LFIA is typically lower than that of molecular (PCR/qPCR) assays, and low-titer infections may escape detection (ELISA and LFIA are often less sensitive than nucleic acid methods) [46]. In addition, antibody cross-reactivity and interference from complex seed matrices can lead to false positives or negatives unless validation and rigorous controls are in place (a known limitation in lateral flow immunoassays) [71]. Particularly for LFIA, the visual readout may be subjective; using quantitative strip readers can mitigate subjectivity but introduces additional costs and logistical burden (e.g., device calibration, power needs, transport) [72].

International and national seed testing organizations (e.g., ISTA, national seed health committees) provide guidelines and proficiency testing schemes that include ELISA for virus testing and recommend validation steps (limit of detection, specificity, pooled-sample strategies). Proficiency testing documents describe how to prepare spiked seed material for ELISA/LFIA validation and performance monitoring. These frameworks are important for harmonizing results across labs and for demonstrating the suitability of immunoassays in certification programs [73]. On the sensitivity enhancements, nanoparticle labels, signal amplification and reader-based LFIA are improving LFIA sensitivity toward ELISA/qPCR levels in some applications [74], and the multiplexing attempts to multiplex ELISA panels and LFIA strips (multi-line membranes, microarray ELISAs) aim to screen for multiple priority pathogens in a single run attractive for seed companies and quarantine services [75]. The workflow integration consists of hybrid pipelines that combine LFIA/ELISA screening with reflex molecular testing (qPCR) and culture/grow-out confirmation are becoming the operational norm in many advanced seed laboratories, while digital capture and AI smartphone readers for LFIA and image analysis of ELISA plates are improving objectivity, traceability, and remote data aggregation [76].

In practice, immunoassays (ELISA and LFIA) are best deployed as screening tools, especially for large seed lots or at point-of-entry inspections, with any positive results then subjected to confirmatory viability or culture-based tests to satisfy regulatory phytosanitary or seed health requirements (e.g., as recommended in seed health chapters). It is critical to validate extraction protocols and pooling strategies for each seed species and target pathogen, to minimize matrix effects or dilution losses that could impair assay sensitivity or specificity (as highlighted in ISTA/ISHI best practice documents). Participating in proficiency testing or inter-laboratory comparisons helps ensure consistency and comparability across laboratories and supports regulatory confidence in the assay results (a key element of ISTA’s accreditation and quality assurance programs). Finally, when feasible, using reader-assisted LFIA devices or plate readers for ELISA reduces subjectivity in interpretation and enables quantitative or semi-quantitative data capture, which is valuable for trend analysis, quality control, and performance monitoring (as discussed in the immunoassay review literature) [77].

The comparison in

Table 1. Comparative overview of conventional and emerging diagnostic assays in seed health testing.

Diagnostic Method	Viability Readout	Time-to-Result	Operator Dependence	Typical Application	References
Blotter/agar plate	High (growth-based)	7–14 days	High (morphology-based ID)	Routine fungal seed tests	[44,68]
Grow-out/seedling assays	High (infection expression)	14–30 days	High	Confirmatory for symptom expression	[44]
ELISA	Low (antigen-based)	4–8 h	Moderate	High-throughput viral screening	[79]
Lateral flow (LFIA)	Low (antigen-based)	5–20 min	Low–moderate	Rapid on-site screening	[64,76,80]

highlights that conventional assays remain essential for seed health testing, particularly when viability or cultural confirmation is required. However, their limitations in speed, standardization, and operator dependence have driven the adoption of molecular, imaging, and AI-based diagnostics discussed in later sections. ELISA and lateral flow immunoassays serve as rapid, affordable screening tools for priority pathogens—mainly viruses—but their inability to confirm viability and lower sensitivity compared to molecular methods means they should not be used as sole evidence for regulatory decisions. The current literature supports integrated workflows where immunoassays provide initial screening, followed by confirmatory molecular or culture-based tests. Technical improvements such as nanoparticle labels, multiplexing, and reader devices are narrowing sensitivity gaps and improving objectivity [76,78].

5. Molecular Diagnostics—Core Methods

Molecular diagnostics provide essential tools for seed health testing by enabling precise detection, quantification, and differentiation of plant pathogens. Techniques such as Polymerase Chain Reaction (PCR), real-time quantitative PCR (qPCR), and digital PCR (dPCR) offer high specificity and sensitivity, especially when extraction methods are optimized for challenging matrices. In regulated seed testing workflows, end-point PCR remains valuable for rapid exclusion and identity verification.

5.1. End-Point PCR and Nested PCR

Classical Polymerase Chain Reaction (PCR) is pivotal due to its robust performance in routine diagnostics, handling scenarios that demand single target checks or when only limited information is necessary [81]. A classical end-point PCR read on agarose gels or capillaries remains useful for single-target identity checks and rapid exclusion screening in routine workflows [42]. Established uses include detection of Fusarium oxysporum f. sp. lactucae in lettuce seeds [82] and Clavibacter michiganensis subsp. michiganensis (Cmm) in tomato seed lots [83], which helped set early molecular benchmarks for regulated pathogens.

Nested PCR increases detection sensitivity by introducing an internal primer set, thereby enhancing the probability of identifying trace pathogens subjected to strong sanitization or environmental challenges. To mitigate contamination, physical separation of setup areas, filtered pipette tips and enzymatic approaches to control amplification carryover are recommended. Nested PCR has been applied to detect Xanthomonas axonopodis pv. allii in onion seeds [84], Alternaria carthami in safflower seeds, Candidatus Phytoplasma asteris associated with Phyllody and Witches’ Broom in pea seeds, and 16 SrI and 16 SrVI phytoplasma in carrot seeds [85]. Seed extracts often contain inhibitors such as polysaccharides, polyphenols, and treatment residues; mitigation includes matrix-adapted extraction, dilution-to-extinction, BSA/PVP additives, or bead/silica clean-ups, plus an internal amplification control (IAC) to detect residual inhibition [86].

5.2. Quantitative and Digital PCR

Quantitative real-time PCR (qPCR) extends PCR from presence/absence to calibrated measurement and high-throughput surveillance. Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE)-compliant protocols, emphasizing calibration range, efficiency, and reproducibility, have led to significant improvements in transparency and inter-laboratory reliability [87]. In seed testing, practical LOD/limit of quantification (LOQ) depend more on extraction recovery and inhibitors than on instrument optics; include process controls (e. g., exogenous spikes or plant targets); and define guard-banded decision thresholds for lot release. Several qPCR assays have been developed for the detection and quantification of seedborne pathogens including Cmm in tomato seeds [88], Xanthomonas translucens pv. undulosa in wheat seeds [89], and Pseudomonas syringae pv. tomato (Pst) [90] and Fusarium oxysporum f. sp. phaseoli in common bean seeds. The multiplex qPCR is used for the detection of Cmm, Pst and pathogenic Xanthomonas species in tomato [91] and simultaneous detection of Colletotrichum truncatum, Corynespora cassiicola, and Sclerotinia sclerotiorum in soybean seeds [92].

When measurements are robust at very low copy numbers or in inhibitor-rich matrices, digital PCR (dPCR) partitions reactions into droplets/nanowells for Poisson-based absolute counting, avoiding standard curves and improving precision near action thresholds. In seeds, dPCR has improved quantitation for Cmm in tomato seeds [93] and Stagonosporopsis cucurbitacearum in seeds of Cucurbita maxima [94]. Notably, dPCR allows for absolute quantitation at very low copy numbers, supporting reliable decision-making even near threshold levels [91]]. Following the dMIQE framework is crucial for achieving reproducible and comparable results across laboratories as well as for publication [95].

5.3. Isothermal Amplification: LAMP, RPA

Isothermal amplification techniques like Loop-Mediated Isothermal Amplification (LAMP) and recombinase polymerase amplification (RPA) provide fast, equipment-minimal alternatives suitable for large-scale screening and field applications. These approaches use multiple primers and strand-displacing polymerases, allowing for rapid results with minimal preparation.

Isothermal amplification offers fast results and minimal equipment, which suits border points and large screening programs. Loop-Mediated Isothermal Amplification (LAMP, ~60–65 °C) uses 4–6 primers with a strand-displacing polymerase and yields results in ≤30 min with turbidity, fluorescence, or color readouts that tolerate partially purified extracts [96].

Recombinase polymerase amplification (RPA) technology employs a coordinated enzymatic system consisting of recombinase, single-stranded DNA binding proteins, and strand-displacing polymerase to facilitate specific recognition and amplification of the target sequence [97]. This process occurs under isothermal conditions, typically within a temperature range of 25 °C to 43 °C. The detection of amplified products is achieved through lateral flow dipstick (LFD) analysis using appropriately designed sequence-specific probes. RPA also integrates with CRISPR/Cas12, which enhance sensitivity, specificity, and rapidness of test from crude sap [98]. RPA assays are established for detection of toxigenic Fusarium verticillioides in maize seeds [99], Pantoea stewartii subsp. stewartii in maize seeds and seedlings [100], and Xanthomonas oryzae pv. oryzae in rice seeds [101]. Both methods generate high amplicon loads; they use closed-tube formats, UDG/dUTP carryover control, pre-aliquoted master mixes, and strict separation of setup and analysis zones. Positive isothermal screens are commonly confirmed by qPCR/dPCR for quantitation or by amplicon sequencing for identification, combining speed with regulatory-grade specificity.

5.4. Reverse Transcription Assays for RNA Viruses/Viroids

RT-PCR/RT-qPCR are the methods of choice for seedborne RNA viruses and viroids. One-step RT-qPCR combines reverse transcription and amplification in a single closed tube, reducing handling and lowering contamination risk while allowing multiplex internal controls [102]. For example, RT-qPCR protocols are validated for detection and quantification of Pepino mosaic virus in tomato seeds [103], Potato spindle tuber viroid (PSTVd) and Tomato chlorotic dwarf viroid (TCDVd) in tomato seeds [104]. Because RNA is inherently unstable, RNA handling must involve RNase-free procedures, stabilization agents (e.g., guanidinium thiocyanate), and robust extraction/RT controls such as armored RNA or transcript-based references. Replicate definitions, Cq acceptance thresholds, and reflex criteria for ambiguous amplification signals should be clearly specified.

5.5. Viability-Linked PCR

Assessing seed viability after pathogen sanitation is challenging with DNA-based diagnostics alone, as nucleic acids from non-viable cells can persist and inaccurately reflect actual risks. Viability-linked PCR employs dye intercalators like propidium monoazide (PMA) or ethidium monoazide (EMA) to distinguish living cells by preventing amplification of compromised genetic material. PMA-based approaches are favored for their specificity and limited impact on viable cells, while complementary RNA-focused techniques offer further validation [105]. PMA-qPCR has been developed to differentiate viable Acidovorax citrulli in watermelon seeds [106]; viable Xanthomonas euvesicatoria, X. gardneri, X. perforans, and X. vesicatoria in tomato seeds [107]; and viable Cmm in tomato seeds [108]. Optimization of dye concentration, light exposure parameters, and quenching conditions for the specific matrix are critical for the reliability of viability PCR assays. RNA-centered approaches, including RNase pretreatment or the interrogation of labile mRNA species, serve as complementary lines of evidence and may be effectively integrated with RT-qPCR for the detection of bacterial and fungal viability. In accordance with best practices, the simultaneous reporting of total DNA and viability PCR-adjusted values, alongside conventional culture-based or grow-out methodologies when applicable, provides a robust framework for defensible lot-level assessments.

seed lot heterogeneity at low prevalence requires risk-based sampling and validated pooling schemes, with seed counts per extract so that results can be expressed as copies per seed or per gram and translated into prevalence estimates. Layer controls: IACs to flag inhibition; extraction/process controls to track recovery; inclusivity panels covering target diversity; exclusivity panels to challenge near-neighbors; guard-band decision thresholds such as Cq cut-offs in qPCR; positivity rules in isothermal assays; copy-number gates in dPCR; as well as repeatability/reproducibility studies under inhibitor challenge, and verification in inter-laboratory settings.

To complement the discussion on molecular diagnostics,

Table 2. Comparative cost-effectiveness of qPCR and LAMP for seed health diagnostics.

Parameter	qPCR	LAMP
Reagent cost per test	$2.50–$3.50 (probe-based chemistry; MIQE-compliant)	$1.50–$2.00 (simpler chemistry; no fluorescent probes)
Equipment cost	Real-time thermocycler ($15,000–$40,000)	Heat block or portable device ($500–$2000)
Time-to-result	60–90 min (thermal cycling + data analysis)	≤30 min (isothermal amplification; colorimetric/turbidimetric readout)
Sensitivity	High (quantitative; validated under MIQE)	High (qualitative; confirmatory qPCR recommended for regulatory use)
Field deployability	Limited (requires lab infrastructure and optical calibration)	High (minimal equipment; suitable for point-of-need testing)
Operator skill	Advanced (trained personnel; calibration of optics)	Moderate (basic pipetting; visual interpretation possible)

Summary: LAMP offers ~25–35% lower reagent costs, minimal equipment footprint, and rapid turnaround, making it ideal for resource-limited or field settings, while qPCR remains the gold standard for quantitative precision and regulatory-grade validation.

provides a direct cost-effectiveness comparison between qPCR and LAMP. Both methods deliver high sensitivity for detecting seedborne pathogens, but their operational profiles differ markedly in reagent costs, equipment requirements, turnaround time, and field deployability. The cost ranges presented are based on typical commercial pricing from widely available diagnostic suppliers and published industry benchmarks. These figures represent approximate averages observed in seed testing laboratories and open-source procurement data, offering practical guidance for laboratories evaluating diagnostic strategies under varying resource constraints.

6. Next-Gen and Meta-Omics

Recent advances in high-throughput sequencing and meta-omics are transforming seed health diagnostics by enabling broad-spectrum screening of microbial communities and detection of low-titer seedborne pathogens. This section reviews three major workflows: amplicon metabarcoding, shotgun metagenomics or targeted capture, and portable sequencing with adaptive sampling. It concludes with a discussion of the bioinformatics, reference databases, and false discovery management strategies that are essential for reliable implementation.

6.1. Amplicon Metabarcoding for Survey Screens

Amplicon metabarcoding uses universal or semi-universal primers targeting conserved genetic loci such as bacterial 16S rRNA, fungal ITS, or animal COI to survey microbial communities in seed materials [109]. The workflow typically involves bulk DNA extraction from seed lots, amplification of barcoding loci, deep sequencing, and bioinformatic clustering into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Compared with single-target assays, metabarcoding allows parallel detection of diverse microbial taxa and provides a cost-efficient approach for broad surveillance [109].

However, several sources of bias can affect accuracy, including primer mismatches, uneven amplification efficiencies, tag switching, and incomplete reference databases [110]. To mitigate these limitations, best practices recommend inclusion of mock community controls, dual indexing, rigorous demultiplexing thresholds, and transparent reporting of clustering and filtering parameters [111].

6.2. Shotgun Metagenomics and Targeted Capture for Low-Titer Pathogens

When pathogen loads are low or when strain-level resolution is required, shotgun metagenomic sequencing provides an untargeted approach by sequencing total DNA from both the host and microbes [112]. In seed health testing, metagenomics has been applied to reconstruct microbial assemblages associated with seeds and seedlings [113].

For very low-titer pathogens, targeted capture or enrichment techniques such as hybridization-based bait libraries or PCR-based enrichment can increase detection sensitivity and reduce sequencing waste [114]. Despite their analytical power, these methods are still challenged by host DNA background, the need for deep sequencing to detect rare taxa, and potential false positives when taxonomic assignment is uncertain [115]. Reliable workflows therefore include statistical thresholds, genome completeness criteria, and independent confirmation of candidate detections.

6.3. Portable Sequencing and Adaptive Sampling

Portable sequencing technologies, particularly those from Oxford Nanopore Technologies (ONT), now make it possible to perform sequencing on site in seed testing laboratories. Adaptive sampling allows selective enrichment of pathogen reads during sequencing, which improves sensitivity without additional sample enrichment. These platforms can provide same-day confirmation of priority pathogens in quarantine or trade inspection contexts, although field-deployable, portable sequencing still requires rigorous sample preparation, quality control, and access to either local or cloud-based bioinformatics pipelines.

6.4. Bioinformatics Pipelines, Reference Databases, and False Discovery Management

Across metabarcoding, metagenomics, and portable sequencing workflows, result reliability depends on robust bioinformatics pipelines, curated reference databases, and effective false discovery management. Standard pipelines include quality filtering, host read removal, taxonomic assignment, abundance normalization, and statistical validation. The limited availability of curated, high-quality reference genomes for many seedborne pathogens remains a major challenge [116].

To minimize false positives and negatives, laboratories should apply transparent parameter settings, include negative and mock controls, set minimum read support thresholds, and confirm rare taxa using orthogonal methods such as qPCR or culture [26]. As computational tools evolve, machine learning classifiers, federated databases and standardized reporting formats are expected to further streamline multi-pathogen seed diagnostics and strengthen data interoperability.

7. CRISPR-Based Diagnostics

Agriculture diagnostics refers to scientific interpretation, detection and monitoring of diseases, pests, nutrition deficiencies and genetic traits in crops, seeds and soil that enable timely measures for input optimization and the development of healthy and high-yielding plants. Diagnostic tools vary across several categories like serological techniques, including ELISA; Immunostrip Tests/LFIs; and Western blotting. These confirm the presence of specific pathogens or toxins in plant tissues based on antigen–antibody interactions [115].

Similarly, biochemical testing involves the study of metabolites, enzymes and biomarkers that reflect a plant’s physiological responses to environmental changes, stress, or nutrient deficiencies. Enzyme assays, isozyme assay and metabolite profiling are common techniques implemented under biochemical testing [26,116]. The non-destructive imaging-based diagnostic technique is evolving rapidly to obtain visual images or spectral signatures using sensors that detect plant stress or damage based on color, temperature or reflectance pattern. X-ray imaging, thermal imaging, hyperspectral imaging and others enable real-time, high-throughput monitoring and can be integrated with AI or ML for precision agriculture application [117,118].

Molecular techniques represent the most sensitive, specific and precise diagnostic techniques as they utilize specific DNA or RNA sequences to determine traits in plants and seeds. These include techniques including Polymerase Chain Reaction (PCR) for amplifying target DNA to detect plant pathogens, DNA Barcoding and Sequencing to identify unknown species or genetic variations, and advance techniques like CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based Diagnostics (SHERLOCK, DETECTR), Loop-Mediated Isothermal Amplification (LAMP) that enable early disease detection, genetic purity testing and pathogen surveillance [119,120].

In the present scenario, integrated diagnostic approaches are implemented, and crop diagnostics are employed for seed and crop health certification; purity testing and pathogen-free propagation material make it an indispensable part of sustainable crop production, biosecurity and global food safety systems [121].

7.1. Evolution of CRISPR-Cas Systems for Agricultural Diagnostics

CRISPR-Cas systems were first characterized as adaptive immune mechanisms in bacteria and archaea, providing defense against invading bacteriophages by recognizing foreign nucleic acids and cleaving them upon reinfection [122,123,124]. These principles underpin modern diagnostic platforms such as DETECTR (Cas12 for DNA targets) and SHERLOCK (Cas13 for RNA/DNA), which generate visual or fluorescent signals upon successful target recognition without the need for complex instrumentation [125,126]. Recent advances have simplified and expanded CRISPR diagnostics: amplification-free Cas12a assays using engineered LbCas12aUltra and optimized stem-loop reporters have achieved ~99.4% accuracy for Candidatus phytoplasma detection and were adapted to instrument-free lateral flow formats suitable for field diagnostics [127,128,129]. Ongoing trends include microfluidic integration, AI-assisted signal quantification, and smartphone-based interfaces for real-time agricultural surveillance aligned with precision agriculture [127].

The core principles of CRISPR-based genome editing—target recognition by guide RNA and sequence-specific nuclease activation—also underpin modern CRISPR diagnostic platforms. Figure 1 illustrates these foundational biosensing steps, including target binding, Cas12/Cas13-mediated collateral cleavage [130], and downstream signal generation across fluorescence, lateral-flow, and electrochemical formats.

7.2. Application of CRISPR Gene Editing on Seed Health Diagnostics

Among CRISPR-associated enzymes, Cas12a (Type V, Class 2 endonuclease) is widely used in diagnostics for its RNA-guided DNA cleavage capability. Upon gRNA binding to the target double-stranded DNA, Cas12a activates trans-cleavage of a fluorophore/quencher-labeled ssDNA reporter, producing a visible fluorescence signal [131,132]. Sensitivity is often enhanced by coupling CRISPR detection with isothermal amplification methods such as LAMP or RPA, which operate at constant temperatures and provide rapid, field-compatible alternatives to PCR [133,134,135,136] These approaches are particularly suited for point-of-care (POCT) and point-of-need (PONT) testing, addressing limitations of PCR such as time, equipment complexity, and operator skill requirements [137,138,139,140].

Several CRISPR-based biosensing platforms have been developed for agricultural diagnostics; for example, DETECTR combines RT-LAMP with Cas12 and employs a reporter molecule for lateral flow assay (LFA) readout [141].

Recent innovations also integrate microfluidics, AI-assisted signal quantification and smartphone-based interfaces, enabling rapid prediction, compact model deployment and accessibility for farmers in resource-limited settings. These approaches enhance precision agriculture and can be extended to detect micronutrient deficiencies—an often-overlooked component of plant health assessment. LFAs remain central to portable diagnostics, with the current research focusing on (i) coupling LFAs with isothermal amplification for ultra-sensitive detection and (ii) novel labeling materials for quantitative, device-compatible readouts. A notable example is the CRISPR/LbCas12a–LAMP biosensor, which demonstrated effective on-site detection of Rice Bakanae Disease (RBD), offering practical crop disease management in field environments [141].

8. Imaging and Non-Destructive Phenotyping

The application of non-invasive imaging techniques with minimal human involvement is highly significant in the agricultural sector and in crop development. Contemporary imaging technologies facilitate the automated visualization of multiple parameters for the characterization of biological specimens, thereby diminishing subjectivity and enhancing the analytical process. Additionally, the integration of two or more imaging modalities has played a crucial role in the identification of novel physicochemical tools and the real-time interpretation of datasets.

8.1. Radiation Imaging (X-Ray, CT and MRI)

Ionizing radiation (IR) is a form of high-energy radiation that possesses sufficient energy to displace an electron (a negatively charged particle) from an atom or molecule, resulting in ionization. This type of radiation serves as a significant asset in agricultural sciences and seed technology, frequently employed to tackle issues related to food microbiological safety and seed storage. Gamma (γ) radiation, a high-energy variant of IR, can penetrate and interact with living tissues [142]. Traditional methods for insect detection— including grain flotation, the Berlese funnel technique, probes and traps, as well as measurements of carbon dioxide and uric acid—exhibit various limitations; they can be subjective, destructive, inaccurate, time-consuming, and ineffective in identifying internal insect infestations [143]. Different insect detection methods vary in specificity: ELISA and PCR target unique insect proteins or genes, while IR detects general chemical groups. Acoustic, electrical conductance, and electronic nose methods rely on insects’ physical activity or emitted chemicals, and acid hydrolysis detects insect waste through chemical reactions [144].

This review paper presents the fundamental principles of imaging techniques and provides a summary of their use in detecting insects and fungi in stored products in real time. Among emerging non-destructive techniques, X-ray imaging, magnetic resonance imaging (MRI), thermal imaging and NIR hyperspectroscopy have been evaluated for rapid and accurate assessment of infestation. Pioneer studies on nuclear magnetic resonance (NMR) have been shown to effectively detect concealed infestations of wheat caused by the granary weevil (Sitophilus granarius), However, the sensitivity achieved was quite low and there have been no significant attempts to employ MRI for detecting stored product pests since that time. The other two advanced methods, near-infrared (NIR) hyper spectroscopy and X-ray imaging, have shown promise for real-time applications [145].

Despite decades of use in seed inspection and quality control, relatively few studies have systematically evaluated the effects of X-ray exposure on seed performance since the 1960s [146]. Existing research has focused primarily on a narrow set of physiological responses, leaving significant gaps in understanding how X-ray exposure influences molecular processes, stress responses, and long-term seed viability. Consequently, further investigation is needed to clarify the mechanisms underlying plant tolerance or sensitivity to X-ray irradiation, particularly as imaging technologies continue to advance and become more widely adopted.

Within this context, computed tomography (CT) has emerged as a promising tool for the non-destructive detection of internal defects and biological contaminants. Studies examining CT-based detection of Sitophilus oryzae infestation in hard red winter wheat have demonstrated high sensitivity at low infestation levels, with reported detection accuracies exceeding 90% under favorable conditions [147]. Slight reductions in detection performance at higher infestation densities have been attributed primarily to imaging artifacts, such as kernel overlap and reduced contrast resolution, rather than fundamental limitations of the technique. These findings underscore the capability of CT imaging to identify concealed insect damage in stored grain and highlight its potential utility as an alternative or complementary approach to conventional inspection methods.

More broadly, advances in imaging technology have substantially expanded the scope of non-destructive seed quality assessment. In addition to conventional X-ray radiography and CT, modalities such as magnetic resonance imaging (MRI) and ultrasound have been investigated for their ability to reveal internal structural defects, density variations, and physiological abnormalities that are not detectable through external inspection [148]. Collectively, these techniques offer new opportunities to assess seed quality, integrity, and health without compromising sample viability, although practical constraints related to cost, throughput, and accessibility continue to influence their adoption in routine seed testing workflows.

8.2. Near-Infrared (NIR)

Near-infrared light penetrates the seed and emits a signal associated with its internal components, which are indicative of seed health. Consequently, NIR can distinguish between low vigor seeds, hidden insects and dead seeds based on the specific type of NIR signal emitted. Furthermore, seeds infected with fungal pathogens produce a distinct NIR signal, allowing for the identification of seeds with fungal contamination. NIR signals have also been utilized to differentiate hybrid watermelon seeds from inbred seeds, as well as to assess the duration required for seed priming to achieve optimal outcomes. While NIR technology is commercially available and used in the seed sector for seed quality assessment, its adoption is still limited due to cost, calibration needs and variability across crops [149,150].

NIR spectroscopy has developed into a rapid, dependable, precise and cost-effective method for the compositional analysis of seed health [151]. This method is applicable for both qualitative and quantitative assessments. The NIR technique yields insights based on the reflectance characteristics of various substances found in a product. It operates on the principle of electromagnetic wavelength absorption within the range of 780–2500 nm. Classical absorption spectroscopy can be employed to ascertain the concentrations of components such as water, protein, fat and carbohydrates. The NIR system is the most effective approach for identifying individual wheat kernels that harbor live or deceased internal rice weevils at different life stages. Machine vision systems are utilized to support grading, cleaning and the diagnosis of diseases and insects within food grain handling operations [152]. At present, there is an increasing application of computer vision systems in seed health for quality assurance, encompassing a spectrum from standard inspections to advanced vision-guided robotic control [153].

8.3. Thermal Imaging

Infrared thermal imaging is widely used in assessing seed quality, including evaluations of germination performance, viability, and vigor. It is also employed for estimating morphological characteristics, detecting diseases and insect infestations, and monitoring seed quality during storage [154]. Depending on the resolution, sensor sensitivity and the range of temporal data captured by thermal cameras, this technique is applied in various contexts. The infrared radiation that thermal cameras capture consists of long-wavelength radiation from the electromagnetic spectrum, which ranges from 0.78 µm to 1000 µm. This radiation is further categorized into near-infrared (0.75–3 µm), mid-infrared (3–6 µm), far infrared (6–15 µm) and extreme far infrared (15–100 µm) radiation. Typically, thermal cameras that operate within the far infrared radiation range are employed for seed quality evaluation and they record surface temperatures accordingly. The choice between active or passive thermal imaging systems is determined by the emissivity, absorptivity, transmissivity and reflectivity of the infrared radiation emitted by the sample [155].

Thermal imaging may serve a pivotal function in this trend and could provide an alternative approach for identifying insect infestations, as the respiration of pests generates heat that exceeds that of the seeds [156]. Lately, thermal imaging has recently emerged as a vital tool for curing diseases and detecting insect infestations in seeds, since the deterioration of seed tissues due to disease progression is typically linked to variations in the surface temperatures of the affected seed areas. An active thermal imaging system was developed [157] and could be used to detect and categorize diseases based on distinct differences in the temperature profiles of infected and healthy seeds. In both the heating and cooling phases, infected seeds exhibited higher surface temperatures than healthy seeds, highlighting the effectiveness of infrared thermal imaging for detecting fungal infections through changes in thermal behavior.

8.4. Multispectral and Hyperspectral Imaging

Multispectral and hyperspectral imaging have been investigated as potential analytical tools for the non-destructive analysis and assessment of seed quality and safety. Hyperspectral imaging, which can acquire spectral and spatial information simultaneously, combines the advantages of spectroscopic and imaging techniques. In other words, it simultaneously obtains the chemical information and the spatial distribution of chemical components in heterogeneous samples [158,159,160]. Additionally, studies conducted by [160,161,162,163] demonstrated the effectiveness of MSI in categorizing various types of damage without the requirement for further analytical evaluations. A comparative summary of non-destructive seed health methods, including hyperspectral imaging, X-ray, machine vision, and thermal imaging, is provided in

Table 3. Comparison of non-destructive seed health methods.

Method	Key Diagnostic Output	Primary Advantage	Primary Limitation	References
Hyperspectral imaging	Special signature, visual image	Identification of vigor, viability, disease and contamination by pathogens	High data volume requires complex processing	[162,163]
X-ray imaging	Internal structure (embryo, endosperm)	Rapidly detects internal defects, insect damage and embryo integrity	Cannot detect internal, biochemical or early-stage problems	[164,165]
Machine vision	External features (size, shape, color)	Fast, automated classification based on visible traits	Cannot detect internal, biochemical or early-stage problems	[166,167]
Infrared thermography	Surface temperature profile	Rapidly assesses physiological activity and vigor	Results can be affected by environmental factors	[168,169]
Electronic nose	Volatile chemical signature and functional properties of legume proteins	Monitors health during storage and detects fungal contamination	Influenced by environmental factors, limited by sensor specificity	[170,171]

.

8.5. Chlorophyll Fluorescence Imaging (CF)

Chlorophyll fluorescence operates on the principle that chlorophyll molecules, when stimulated by light of a specific wavelength (typically within the blue or red spectrum), emit light of a longer wavelength (fluorescence) as they revert to their ground state [172]. This fluorescence signal can be quantified to evaluate various physiological maturity states of the seed, especially those associated with the functionality of the photosynthetic apparatus. The commercial application of this principle allows for the assessment of chlorophyll fluorescence in the seed coat or embryo, aiding in decision-making regarding harvest timing, maturity and seed viability.

9. Artificial Intelligence (AI) and Machine Learning (ML) in Seed Diagnostics

Artificial intelligence and machine learning have emerged as transformative tools in seed health diagnostics, enabling rapid, non-destructive, and highly scalable analysis of complex datasets. These technologies leverage advanced algorithms to interpret visual, spectral, and molecular data, reducing reliance on manual assessments and improving diagnostic accuracy under low pathogen prevalence. By automating feature extraction and pattern recognition, AI systems can detect subtle indicators of infection, classify seed quality traits, and predict contamination risks with minimal operator bias. Among the various approaches, deep learning models, particularly convolutional neural networks, have shown exceptional performance in processing high-dimensional imaging and hyperspectral data, laying the foundation for next-generation diagnostic workflows. Beyond performance metrics, transparency is critical for regulatory acceptance of AI-driven seed diagnostics. Explainable AI (XAI) techniques—such as Gradient-weighted Class Activation Mapping (Grad-CAM) and SHAP (SHapley Additive exPlanations)—enable visualization of model decision pathways, highlighting which image regions or spectral features influence predictions. Incorporating these interpretability tools into validation workflows strengthens trust and ensures compliance with ISO/ISTA guidelines.

9.1. Convolutional Neural Networks (CNN) for Image and Spectrum Classification

Convolutional Neural Networks (CNNs) have become the go-to deep learning workhorses for seed image analysis and pathogen detection across both image and spectral datasets. CNNs excel at automatically extracting hierarchical features from seed images, enabling identification of morphological traits linked to seed health or pathogen presence without manual feature engineering. Recent work has reported CNN accuracies exceeding 99% in seed quality classification tasks [173]. CNN architectures have also been adapted for seed health diagnostics. For example, hyperspectral imaging of rice seeds (874.41–1734.91 nm) combined with CNNs enabled detection of Fusarium spp., achieving accuracies above 98% [174]. The study demonstrated that CNNs consistently outperformed traditional machine learning models—including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machines (SVM)—which typically achieved accuracies above 90%. This performance gap reflects CNNs’ ability to learn complex, nonlinear patterns in spectral data without handcrafted features.

Transfer learning strategies using pre-trained CNNs have been particularly effective when seed health datasets are limited. Advanced architectures such as EfficientNet variants have been tailored for seed disease detection. MobileNetV2, enhanced with layers such as average pooling, flattening, dense units, dropout, and softmax activation, reached ~96% accuracy across four corn seed disease classes (Broken, Discolored, Silk cut, and Pure) using 21,662 images. Performance gains were further supported by data augmentation, adaptive learning rate schedules, model checkpointing, and dropout regularization. MobileNet with transfer learning has also achieved 99.55% accuracy in rice seed classification; freezing the first twelve convolutional layers and fine-tuning only the final layer allowed effective learning from a 10,000-image dataset spanning five seed classes [175].

Hyperspectral imaging in the 400–1000 nm range has enabled identification of Aspergillus flavus in naturally infected peanut seeds. Integrating CNNs with near-infrared hyperspectral imaging (935–1700 nm) and analytical techniques such as Wavelet Packet Analysis (WPA) and Principal Component Analysis (PCA) has further strengthened non-destructive seed health assessment pipelines. SVM-based hyperspectral approaches allow 100% seed testing without destroying samples, offering a major advantage over traditional microbiological assays. These methods have achieved 100% accuracy in detecting Aspergillus spp. in naturally infected corn kernels. Ensemble boosting algorithms—including CatBoost, Gradient Boosting Decision Trees (GBDT), and XGBoost—have also demonstrated robust performance, each exceeding 97.42% accuracy under diverse contamination levels and seed conditions [176,177].

Transformers and Classical Machine Learning for Image and Spectrum Classification: Transformer-based models, originally developed for natural language processing, have been effectively adapted for computer vision tasks in agriculture, improving the identification of disease indicators in seed health assessment [178]. Vision Transformers (ViTs) use self-attention mechanisms to capture global contextual information in images, offering advantages over traditional CNNs in applications where long-range feature relationships matter. Implementations such as Swin Transformers achieve computational efficiency through reduced parameter counts while preserving high accuracy by using hierarchical feature representations built through shifted-window attention. These models have demonstrated strong performance in plant disease detection, with some studies reporting accuracies of up to 95.5% and outperforming architectures like DenseNet-121, VGG-16, and standard CNNs [179,180]. The inherent attention mechanisms in transformer architectures also enable interpretable visualizations that highlight image regions influencing classification decisions.

9.2. Classical Machine Learning for Image and Spectrum Classification

Traditional machine learning remains a steady workhorse in seed health testing, especially for spectral analysis, feature-based image classification, and situations where training data are scarce. Classical models such as Support Vector Machines (SVMs), Random Forests (RFs), and ensemble methods offer interpretable decision boundaries and often outperform deep learning when datasets are small or when explainability is essential. These approaches have been successfully used to classify seed and seedling quality using descriptors generated through interactive machine learning workflows, even with limited sample sizes [178].

Support Vector Machines (SVMs) are widely applied for seed discrimination and classification based on morphological, color, and spectral features. For example, SVM models have achieved over 94% accuracy in identifying peanut seeds contaminated with various fungal species using hyperspectral data spanning 967–2499 nm, demonstrating strong performance in high-dimensional spectral spaces. The kernel trick allows SVMs to manage nonlinear decision boundaries effectively. With appropriate kernel functions—such as radial basis function, polynomial, and sigmoid—SVMs consistently perform well across seed species and pathogen types. Least Squares SVM (LS-SVM) models have also been used to detect cucumber green mottle mosaic virus in watermelon seeds, achieving 92% accuracy with hyperspectral imaging between 950 and 2500 nm [181,182].

SVM’s popularity stems from its ability to handle complex, high-dimensional datasets typical of hyperspectral imaging in seed health diagnostics. However, Random Forest (RF) classifiers are equally important in many contexts due to their ensemble structure, which reduces overfitting and provides interpretable feature importance rankings—critical for regulatory audits. While SVM excels in precision for small, high-dimensional datasets, RF offers scalability and robustness to noise, making it suitable for diverse seed conditions and mixed pathogen profiles.

Random Forest classifiers and ensemble methods are equally valuable due to their capacity to aggregate multiple decision trees, improving predictive stability and reducing overfitting—common challenges in high-dimensional biological datasets. RF models deliver robust predictions across diverse seed conditions [183]. In comparative studies, RF has demonstrated superior performance over SVM, C4.5, and AdaBoost, and in dry bean classification for seed certification, RF combined with PCA achieved 95.5% accuracy, outperforming k-nearest neighbors (95%) and SVM (93.5%) [184,185].

Classical discriminant methods such as Linear Discriminant Analysis (LDA) also remain effective for spectral classification in seed health assessments. LDA and PLS-DA have achieved accuracies above 92% when identifying Fusarium-damaged hard wheat kernels using near-infrared hyperspectral imaging (938–1654 nm). Other studies applying LDA in combination with Quadratic Discriminant Analysis (QDA) or Multiple Discriminant Analysis (MDA) have reported over 90% accuracy for detecting Aspergillus glaucus in canola seeds using hyperspectral data between 1000 and 1600 nm [186,187].

9.3. Dataset Curation and Labeling Strategies for Seed Diagnostics

Effective AI or ML in seed health testing depends on well-curated datasets that capture the full range of seed conditions and pathogen interactions, since dataset quality ultimately shapes model performance and generalizability. Dataset development for seed health applications requires systematic collection across multiple varieties, growing seasons, and storage conditions [188]. Sample sizes vary widely, from fewer than 100 seeds to more than 47,000 samples, with larger datasets generally supporting stronger model training. However, targeted augmentation methods can partially offset limited sample sizes [189].

A key consideration during dataset creation is how infected seed samples are generated. Naturally infected seeds (54.5%) better reflect real field conditions, while artificially inoculated seeds (40.9%) allow controlled experimentation with specific pathogens. Although natural infections still require rigorous validation, laboratory-inoculated seeds have been shown to reliably mimic field-infected kernels [190].

Agricultural datasets are inherently rich and multi-modal, incorporating imaging, spectral, and genomic data. Handling these diverse data types demands careful standardization to maintain consistency and interoperability across sources [188]. Centralized and standardized dataset frameworks also support efficient deep learning workflows, especially when models are fine-tuned from general non-agricultural datasets [190]. Strengthening standardizations is particularly important because existing public agricultural datasets remain limited in size and structure, making it difficult to fully leverage higher-capacity models and modern computational power for seed health applications [191].

9.4. Labeling Strategies for Seed Diagnostics

Accurate labeling and validation rely on combining multiple confirmation methods. This requires close attention to pathogen-specific traits while drawing on molecular and spectroscopy tools such as DNA sequencing, qPCR, LAMP, MultispeQ, and hyperspectral imaging. These approaches strengthen model development and improve disease detection by boosting both specificity and sensitivity. They also support disease management, monitoring, and germplasm screening by enabling discrimination between closely related strains before symptoms appear.

Advanced imaging—particularly hyperspectral and infrared thermal technologies—further enables early detection of diseases that were previously invisible in the field [192,193,194,195].

Seed health studies commonly rely on visual confirmation (54.5%), molecular diagnostics such as PCR (13.6%), and culture-based methods (22.7%). Many studies omit confirmatory validation, even though molecular methods provide the highest specificity, albeit at a higher cost and with increased sample destruction [196].

Robust quality control includes inter-rater reliability assessments and alignment with established diagnostic standards. Integrating plant pathology expertise into AI development ensures biological relevance and technical accuracy. Hierarchical labeling frameworks enable both high-level categories (e.g., healthy vs. infected) and fine-grained, pathogen-specific identification [197]. Finally, combining omics data with advanced imaging provides deeper insight into plant–pathogen interactions. These integrated datasets help reveal new intervention targets for disease management [198].

9.5. Dataset Augmentation and Class Imbalance for Seed Diagnostics

Data augmentation is essential for mitigating class imbalance, a recurring issue in seed diagnostics where healthy seeds vastly outnumber pathogen-infected ones. Standard augmentation workflows rely on geometric transformations (e.g., rotation, scaling, translation, flipping) and photometric adjustments (e.g., brightness, contrast, color balance) to expand dataset diversity without altering biological relevance.

Generative Adversarial Networks (GANs) further strengthen diagnostic performance by producing synthetic seed images that mimic real pathogen-induced characteristics. Studies applying Deep Convolutional GANs (DCGANs) and CycleGANs to agricultural disease datasets report notable gains in accuracy and recall, particularly for detecting cotton Fusarium wilt, where GAN-generated images effectively captured the underlying distribution of the training data and alleviated limited-sample constraints [198,199].

To address class imbalance more directly, the Synthetic Minority Over-Sampling Technique (SMOTE) remains the most widely adopted strategy. SMOTE generates new minority-class samples by interpolating feature space vectors between existing examples and their nearest neighbors [200,201]. Advanced variants—including Borderline-SMOTE, Distance-Based SMOTE (D-SMOTE), and Bi-Phasic SMOTE—focus on regions near decision boundaries, where misclassification risk is highest, thereby improving model sensitivity to subtle pathogen signatures.

9.6. Model Explainability and Performance Metrics for Seed Diagnostics

Model explainability is essential in seed health diagnostics, where regulatory compliance and diagnostic accuracy require transparent and traceable decision-making. Techniques such as Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) identify which regions of a seed image contribute most to a model’s prediction. Grad-CAM generates heatmaps by computing the gradients of a predicted class score with respect to the activations of the final convolutional layer, allowing clear visualization of the spatial features that influence classification outcomes. In agricultural imaging, Grad-CAM and Grad-CAM++ have effectively highlighted pathogen-associated regions in plant and seed images, enabling interpretable assessments of model behavior and facilitating the detection of potential biases or image artifacts that may compromise diagnostic reliability [202].

Integrating explainability into seed diagnostic workflows strengthens confidence in AI-assisted decisions by ensuring alignment with expert knowledge and established diagnostic criteria. Visual explanations support verification models that rely on biologically meaningful features rather than non-informative correlations. Recent studies increasingly combine multiple interpretability methods—LIME, SHAP, and Grad-CAM—to provide complementary perspectives on model reasoning, capturing both global feature contributions and localized spatial attributions [203,204].

Performance metrics for AI/ML models in seed health assessment become especially tricky under imbalanced datasets and very low pathogen prevalence. Even highly specific assays can produce a low probability of true infection among positive results, underscoring the need for a practical interpretive framework when pathogen incidence in seed lots falls below 1% [205]. Models often appear highly accurate because healthy seeds dominate; however, they typically struggle with rare positive cases. As a result, they may show high sensitivity but low positive predictive value (PPV), leading to elevated false positive rates and costly, unnecessary interventions.

Receiver Operating Characteristic (ROC) curves visualize the trade-off between true positive and false positives rates across thresholds, with ROC-AUC providing a threshold-independent score. Yet for strongly imbalanced datasets—a constant reality in seed health—Precision-Recall (PR) curves provide a more meaningful evaluation [206]. PR curves directly compare precision (PPV) and recall (sensitivity), making them better suited to situations where true positives are extremely rare. Empirical work confirms that PR-AUC outperforms ROC-AUC as an indicator of model quality under such conditions [207].

A thorough assessment should include accuracy, precision, recall, and F1-scores to fully interpret model behavior in low-prevalence scenarios. Merged or composite scores can further clarify performance under class imbalance. Considering both PR-AUC and the F1-score is critical for seed health diagnostics because these metrics help identify rare, infected seeds while limiting false positives that drive economic losses. Differentiating healthy from infected high-value vegetable seed lots requires careful evaluation across multiple thresholds, as threshold choice directly influences precision, recall, and downstream commercial viability [208].

In commercial seed lots—typically showing pathogen incidences near 1%—PR curves remain sensitive to genuine performance shifts, while ROC curves may show inflated AUC values that do not translate to practical usefulness. Understanding PPV and Negative Predictive Value (NPV) is essential in this context. Low disease prevalence depresses PPV even when specificity exceeds 99%, resulting in many false positives, whereas NPV remains high. This imbalance directly affects decisions regarding seed lot acceptance. Under low prevalence, any positive screen requires confirmatory testing to avoid unnecessary rejections. Meanwhile, high NPV supports using AI-based screening to efficiently identify clean lots needing minimal follow-up.

Effective AI-driven workflows must therefore account for both the true detection of infected seeds and the misclassification of healthy seeds, since each outcome affects economic feasibility. Hypothetical model studies show that optimizing precision and recall is necessary to meet eligibility thresholds across seed-sorting pipelines involving pathogens with different prevalence levels. High average precision reflects strong identification of true positives, especially in datasets that weight the positive class. Although the F1-score balances precision and recall, it does not capture behavior across all thresholds the way PR-AUC does—an important limitation when evaluating the reliability of positive classifications [209,210].

Notably, AI models in seed diagnostics must not remain “black boxes.” Techniques like Grad-CAM generate heatmaps of influential image regions, while SHAP values quantify feature contributions across hyperspectral bands. These artifacts should be captured during model validation and archived alongside performance metrics. Explainability supports regulatory audits, mitigates bias, and aligns with emerging standards for trustworthy AI in agriculture.

Adjusting model thresholds substantially shifts the balance between reducing false positives (higher precision) and reducing false negatives (higher recall), and these trade-offs directly influence economic outcomes in seed testing programs [211]. By calibrating precision and recall appropriately, seed companies can generate sorted fractions that meet specific quantitative and qualitative requirements [212]. As shown in Figure 2, ROC and Precision-Recall (PR) curves demonstrate how diagnostic performance metrics diverge under low-prevalence conditions, emphasizing the importance of PR analysis for rare seedborne pathogens.

9.7. Edge Deployment vs. Cloud, MLOps in Accredited Labs, Reproducibility and Versioning

Edge-to-cloud decisions shape the performance, cost, and accessibility of AI-driven seed health systems. Choosing between local (edge) and cloud-based processing always comes with trade-offs in compute power, latency, data privacy, and infrastructure needs.

Edge systems that process data directly inside seed testing labs cut latency, strengthen data security, and keep diagnostics running even when connectivity is spotty. But the flip side is real: updating or retraining models on these devices is harder because of limited compute power and irregular internet access [213]. For that reason, cloud platforms usually handle the heavy lifting—initial model training, large-scale analytics, and fine-tuning on high-performance, AI-optimized hardware. Models are then adapted and deployed onto edge devices for fast, on-site inference.

This hybrid setup blends the strengths of both worlds. The cloud supports computationally demanding development and large dataset analysis, while the edge enables real-time decision-making inside agricultural environments. Together, they address the core challenges of applying AI in agriculture by pairing deep analytical power with immediate, field-level insights [214].

More advanced hybrid architectures—especially those using federated learning—push this further. By keeping sensitive agricultural data closer to where it originates, they improve privacy and reduce reliance on continuous connectivity. At the same time, they still benefit from shared model improvements across sites. This approach is designed to strengthen accuracy, reproducibility, and version control in accredited seed labs, ensuring consistent performance even across distributed testing environments.

Federated and distributed AI systems are redefining how plant disease detection is carried out, offering stronger generalization across agricultural contexts while reducing communication overheads and training costs. These efficiencies make earlier disease identification far more attainable [214]. Decentralized learning also enables the formation of grower federations that can share model insights securely and transparently, strengthening collective forecasting and production decision-making [215]. Advanced neural architectures—including convolutional neural networks and Vision Transformers—continue to drive progress in detecting leaf diseases and other crop anomalies, mitigating the substantial economic losses and quality reductions associated with plant health issues [216,217].

Federated learning directly addresses data heterogeneity and privacy constraints across farms by supporting joint model training without raw data exchange, producing robust disease classification models from diverse datasets [218]. By allowing insights gained on one farm to inform others, federated systems nurture a collaborative, resilient ecosystem for disease surveillance and management [219]. Demonstrated successes in agriculture highlight the strong performance of federated approaches using convolutional architectures and attention mechanisms trained on geographically and biologically diverse image sets [216].

Tiny Machine Learning (TinyML) further shifts diagnostics toward true edge intelligence, enabling efficient deployment of compact models on low-power embedded devices. Models under 140 KB can now achieve ~96% accuracy while consuming only 2.63 mW/h, making continuous, battery-powered plant-health monitoring feasible [217]. Boards such as the Arduino Nano and ESP32 support real-time inference for inline seed testing. Compression methods—quantization, pruning, and knowledge distillation—preserve model accuracy while reducing computational load by 4–10×, making sophisticated inference viable directly on seed testing hardware.

Cloud infrastructures complement these edge systems by providing the computational depth needed to train large-scale models and process high test volumes. High-performance GPUs, distributed training environments, and the ability to pool diverse seed datasets enable ongoing model refinement and generalizability. Centralized storage allows rapid incorporation of new data, producing stronger models over time. However, cloud dependence introduces latency concerns for fast-turnaround diagnostics and raises privacy considerations in competitive seed markets. Hybrid cloud–edge architectures therefore provide an optimal compromise: edge devices handle initial screenings and transmit flagged samples or summary features to the cloud for deeper analysis, model updates, and cross-location learning.

In accredited seed testing laboratories, integrating machine learning operations (MLOps) resolves the practical challenges of deploying and maintaining AI models under strict regulatory and quality assurance expectations. Robust CI/CD frameworks support continuous training, validation, and deployment with full auditability—critical for labs operating under ISTA or ISO/IEC 17025 accreditation [220,221,222]. Automated testing monitors model accuracy against defined benchmarks and alerts personnel when performance drifts.

Seamless integration with laboratory information systems preserves established QA workflows while enabling efficient AI-supported diagnostics. Version control ensures complete traceability for datasets, model parameters, and configuration changes. Continuous monitoring tracks key operational indicators—including prediction accuracy, throughput, and system reliability—to maintain both diagnostic validity and regulatory compliance [223]. Drift detection algorithms identify shifts in seed varieties, imaging conditions, or storage environments that could degrade performance.

Quality assurance for AI-based seed health analysis relies on routine validation against reference benchmarks, inter-laboratory comparison studies, and proficiency testing programs. These oversight mechanisms ensure that deployed models remain trustworthy and aligned with accreditation criteria over time. Emerging audit-based certification systems may soon automate evaluation of MLOps lifecycle artifacts using standards such as ISO 25012 [224], enabling rapid issuance or revocation of compliance certificates as models evolve.

10. Interferences, Pitfalls, and QC

Seed health testing is inherently sensitive to a range of technical interferences and operational pitfalls, which can jeopardize its diagnostic performance if left unaddressed. With rapid adoption of high-throughput technologies to improve speed, sensitivity, cost, and to detect latent infection, the need for well-defined guardrails has become even more critical for quality assurance [225]. However, the reliability of seed health assays is constantly challenged by seed matrix inhibition, chemical residue from treatments, and the persistent risk of cross-contamination or amplicon carryover [226,227,228]. Addressing these vulnerabilities is critical to ensure consistent interpretation of diagnostic methods across laboratories and reliable decision-making in seed health testing.

Emerging diagnostic platforms are expanding the frontiers of seed health testing, as the integration of high-throughput platforms offers a paradigm shift in phytopathology and plant protection strategies worldwide. While these tools are powerful and, in many cases, non-destructive in nature, they can amplify risks if control strategies are overlooked [229]. Accordingly, appropriate controls are the cornerstone of robust diagnostic workflows to mitigate false positive and false negative results [230]. Controls are broadly categorized into process controls (positive/negative), amplification controls (positive, negative, and non-template), and internal amplification controls (IACs). Process controls verify method sensitivity, specificity, and confirm that the reagents and tools function properly [231]. Positive process controls are known samples containing the target organism that are processed alongside test samples to verify detection performance, while negative process controls contain no target organism and confirm the absence of contamination or false positives; additional process controls, including mock communities or spike-ins, help identify contamination or cross-reactivity during sample handling, extraction, and downstream processes [232,233]. Amplification controls monitor the integrity of a workflow from sample processing to analysis. These include non-template controls (DNA or RNA free water) to check for contamination, positive amplification controls (DNA or RNA from reference isolates for each primer, especially in multiplex PCR) to confirm assay performance, and negative amplification controls (DNA or RNA from non-target isolates) to ensure assay specificity [234]. IACs are non-target nucleic acid sequences co-amplified with target sequences to monitor assay efficiency and guard against detection inhibition [234]. The necessity of these controls varies with the diagnostic platform and are implemented as either essential or optional measures. Together, these layers of controls provide comprehensive quality assurance critical for accurate interpretation of results and maintaining reproducibility across technicians, laboratories, and diagnostic platforms (Supplemental Table S1).

Seeds are composed of complex metabolites, including phenolic compounds, lipids, and polysaccharides, which can co-purify during nucleic acid extraction and suppress target detection [235]. The inhibition is particularly problematic in molecular-based assays, highlighting the need for careful sample preparation. To mitigate these inhibitory effects, optimizing extraction protocols, such as incorporating magnetic bead-based purification, is essential, while the inclusion of IACs remains indispensable to monitor the sensitivity of the detection assay [224]. On the other hand, machine learning models trained on limited or poorly annotated datasets can introduce bias, leading to systematic misclassification of diagnostic results [236]. In the absence of robust validation controls, such as algorithm audit trails and curated reference datasets, AI systems may generate outputs that are difficult to verify, undermining both trust and regulatory acceptance while risking financial loss and eroding confidence in innovative diagnostic technologies [237]. Ultimately, the reliability of diagnostic methods depends on robust sample preparation and thorough validation to ensure both scientific rigor and trust in routine applications.

Seeds are exposed to various physical or biological treatments to manage pests, diseases, and safeguard seed quality during storage and production cycles [238,239]. Their widespread application adds additional layers of complexity to diagnostic pipelines, as the residue from these treatments can influence the analytical performance of seed health tests. In nucleic acid extraction-based assays, treatment residue can chemically modify or degrade target DNA or RNA templates, resulting in poor extraction yield and reduced amplification efficiency [240]. Emerging AI-based diagnostics add yet another layer of vulnerabilities. Algorithms trained on untreated data fail to establish treatment-related spectral or morphological changes, requiring domain-appropriate training data and validation to ensure diagnostic robustness [199]. To mitigate these effects, the assay must be validated on both treated and untreated seed lots to account for chemical interference by incorporating process controls and reference materials. High-throughput molecular diagnostic platforms (e.g., PCR, next-gen, meta-omics, etc.) are particularly prone to contamination, where traces of DNA or RNA from previous reactions and carryover amplicons are the biggest cause of false positive results [241]. This contamination can aerosolize during sample preparation, pipetting, or other downstream operational processes, allowing the contaminant template to spread and generate spurious results [242]. For imaging and AI-based diagnostics, such as fluorescence or hyperspectral imaging, inadequate calibration or poorly defined reference standards can distort classification outcomes [199]. Similarly, without rigorous negative and positive controls to benchmark infected versus healthy samples, subtle changes in lighting, instrument drift, or seed-surface properties may be misinterpreted as pathogen signals. This compromises reproducibility across laboratories and reduces confidence in adopting these methods as part of routine seed quality assurance [243,244].

In addition to inhibition, contamination remains a critical source of false results. Strengthening laboratory hygiene, workflow separation, routine swab testing, and timely calibration of equipment can help safeguard the precision of diagnostic methods. Ensuring repeatability and reproducibility is critical to obtain credible seed health testing across all diagnostic platforms. In molecular assays, different nucleic acid extraction protocols, reagents, thermal cycle calibrations, or instrumental sensitivity introduce variation, especially for low-titer pathogen results [245]. Imaging diagnostic platforms face analogous challenges, where sensor resolution, changes in illumination, or seed orientation can alter spectral assessments, undermining comparability between laboratories [246]. Algorithm performance depends on training data diversity and quality, such that a model trained on one seed population or treatment type could yield inconsistent predictions between laboratories [247]. Reference materials and calibrated standards, such as well-characterized seed panels and standardized imaging databases, can serve as benchmarks between technicians and laboratories [248].

Establishing standardized processes, robust quality controls, and shared reference frameworks is essential to ensure repeatability, reproducibility, and compatibility in seed health testing across laboratories and regions.

As seed testing moves toward integrated molecular, imaging, and AI approaches, unified quality assurance frameworks will be key to building trust, supporting trade, and protecting global food security. Guard-banding decision thresholds are a critical safeguard to reduce uncertainty across diagnostic platforms. In molecular assays, this means retesting borderline Ct values; in imaging, it involves adjusting classification cut-offs; and in AI systems, it requires confidence scoring and human review of ambiguous outputs. Cross-platform harmonization through standardized controls, calibration, curated datasets, and inter-laboratory validation is essential to ensure repeatability and reproducibility. Similarly, the publicly available databases and reference panels will play a key role in promoting consistent and reliable seed health results.

11. Performance and Economics

Rapid, reliable, and cost-effective diagnostics are essential to safeguard global agriculture against emerging seedborne pathogens. Diagnostic tools range from traditional visual checks and culture-based or bioassay methods (direct approaches that confirm viability and pathogenicity) to advanced molecular, imaging, and AI-driven approaches (indirect methods). Each offers unique trade-offs in terms of viability confirmation, turnaround time, cost per sample or lot, accuracy, and environmental impact [188,189].

A major challenge in seed health testing is decision-making under low pathogen titers. Although the likelihood of an outbreak is low, the consequences of missing an infection can be severe. Understanding assay sensitivity and specificity is therefore critical for confidence in diagnostic results, guiding interpretation, and defining proportional risk mitigation strategies [238]. Under low prevalence, the sensitivity–specificity trade-off becomes pronounced. Highly sensitive assays (e.g., molecular or nucleic acid-based tests) reduce false negatives and enable early detection, supporting quarantine and exclusion programs [238,249]. However, increased sensitivity can also raise false positive risks, especially when detecting non-viable or trace nucleic acids, potentially triggering unnecessary interventions, retesting, and trade or financial losses [250].

To manage these trade-offs, diagnostic guardrails are indispensable. These include assay validation, threshold definition, confirmatory workflows, and the use of control materials across test stages. Traditionally, tiered diagnostic methods have been the gold standard—combining indirect prescreen assays (molecular, serological, or imaging-based) with confirmatory direct assays (bioassay or culture-based tests) to enhance efficiency and reliability [230]. Recent advances show that molecular approaches can achieve comparable accuracy to conventional bioassays with significantly faster turnaround times [251]. Moving forward, coordinated efforts among industry, academia, and regulators are needed to refine standards, define acceptable uncertainty thresholds, and institutionalize guardrails for timely, cost-effective decisions under low-titer scenarios.

Beyond accuracy, seed health testing must also be operationally feasible. Conventional methods rely on visualizing pathogen growth on seeds, providing definitive evidence under favorable conditions [252]. However, these phenotypic methods often lack sensitivity and specificity, making it difficult to distinguish closely related species. For example, culture plating may fail to differentiate morphologically similar species, posing regulatory challenges when one species within a genus is of quarantine importance [253]. While direct methods are inexpensive, they are time-consuming (days to weeks) and have low throughput. In contrast, molecular diagnostics deliver faster results—often within hours—with improved sensitivity and specificity. They can detect pathogens that are difficult to culture and differentiate species with high genetic similarity [234]. Automation and multiplexing further enhance throughput but increase operational costs for reagents, instruments, and skilled labor. Imaging-based diagnostics offer non-destructive, high-throughput detection with rapid turnaround, though initial equipment costs are high; per-sample costs decrease significantly at scale.

Integration of AI and ML-driven diagnostics into molecular and imaging workflows is transforming turnaround time and throughput. These technologies enable automated pattern recognition, anomaly detection, and predictive analysis for real-time decision-making in seed health programs [254,255]. While AI requires substantial upfront investment for model development and training, the cost per lot decreases once systems are optimized. Future research should focus on standard validation workflows, cost-effective automation, and robust data pipelines to ensure scalability and operational convenience. Bridging next-generation technologies with regulatory guardrails will be key to achieving diagnostic precision and operational sustainability.

Successful implementation also depends on alignment with international regulatory frameworks and seed movement logistics. Seeds often cross multiple borders during production, creating risks of accidental or deliberate pathogen introduction [256,257]. Harmonized, science-based phytosanitary requirements are critical for resilient and transparent global seed movement [258]. Variability in diagnostic protocols and interpretation among national plant protection organizations (NPPOs) can lead to inconsistent risk assessments and trade delays, underscoring the need for global alignment in tools, reference materials, and standards [250].

Recent advances enable data-driven, risk-based decision frameworks to guide actions such as quarantine, eradication, or commercial release of seed lots. Multiple checkpoints throughout production—from site selection to post-harvest conditioning, treatment, storage, and distribution—create integrated surveillance for early detection and proactive risk management [259]. A harmonized diagnostic framework enhances transparency, reduces trade disruptions, and strengthens global commitment to sustainable seed production. Building shared data platforms, reference libraries, and joint validation programs among NPPOs, research institutions, and industry will support a trusted diagnostic ecosystem anchored by harmonized guardrails.

Finally, sustainability is emerging as a critical dimension. Both direct and indirect testing methods consume water and energy and generate chemical and plastic waste, contributing to environmental impact. Green lab initiatives—such as energy-efficient equipment, workflow optimization, and recycling programs—are increasingly adopted to mitigate these effects [5]. Integrating diagnostics with green practices supports institutional sustainability goals and reduces resource use and costs. Environmental guardrails, including carbon accounting, eco-friendly consumables, and waste-minimizing benchmarks, can further embed sustainability without compromising analytical quality [260]. These measures strengthen compliance with emerging environmental regulations and stakeholder confidence in sustainable seed systems [261,262].

In summary, seed health testing must balance operational efficiency, diagnostic performance, regulatory compliance, and environmental responsibility. Accurate and timely detection requires harmonized standards, scalable automation, and AI-enabled approaches, integrated with sustainability practices. This convergence will define next-generation seed health testing as scientifically rigorous, economically viable, and environmentally responsible—capable of supporting global trade in an evolving agricultural landscape.

12. Case Studies in Seed Health Diagnostics

12.1. Viral Pathogens: Tomato Brown Rugose Fruit Virus (ToBRFV) in Tomato and Pepper

Tomato brown rugose fruit virus (ToBRFV) serves as a reference pathogen for understanding seedborne contamination versus biological transmission in solanaceous crops. Studies consistently show contamination is predominantly external—localized to the seed coat and occasionally the endosperm—while the embryo is typically virus-free [263]. Transmission rates to seedlings are low (≈2.8% in cotyledons; ≈1.8% in third true leaves) and true seed-to-seedling transmission frequencies can be near 0.08% under production conditions [264,265]. These findings reinforce two critical points: (i) surface contamination does not equate to infectivity, and (ii) molecular positivity does not guarantee biological transmission. In several disinfection treatments, infectivity was lost while viral RNA remained detectable by RT-qPCR, underscoring the need for viability-aware interpretation and confirmatory workflows when regulatory decisions are at stake [264].

Practical implications: In humid tropical environments, RNA degradation and matrix inhibition (polysaccharides, polyphenols) are common issues. Mitigations such as polyvinylpyrrolidone (PVP) or bovine serum albumin (BSA) during extraction, dilution-to-extinction, and silica/magnetic bead clean-ups markedly improve RT-qPCR performance [89]. Composite sampling with method-appropriate pool sizes paired with reflex testing supports cost-effective surveillance while maintaining sensitivity; pooling limits should follow ISTA/EPPO protocols [18,20]. For quarantine contexts, a tiered workflow (RT-qPCR prescreen→grow-out or bioassay confirmation) aligns analytical sensitivity with biological relevance [263,264].

12.2. Bacterial Pathogens: Xanthomonas campestris on Brassica Seeds

Black rot caused by Xanthomonas campestris pv. campestris (Xcc) remains a top phytosanitary concern for Brassica seed production and trade. Modern multiplex real-time PCR assays reach analytical sensitivities of one infected seed among 10,000 screened, with internal plant DNA controls to flag inhibition [266]. Infection localization studies reveal that contamination from fungal and bacterial seedborne pathogens can be superficial, but under high inoculum pressure, limited penetration into internal tissues (endosperm, embryo) can occur [15,16,267]—explaining occasional disinfection failures and motivating viability-linked or grow-out confirmation in borderline cases [267]. At the commercial scale, validated disinfection treatments (e.g., 3% hydrogen peroxide for 30 min) eliminate detectable Xcc while maintaining high germination (~95%), though temperature, drying, and lot size influence reproducibility [268].

Practical implications: Pooling reduces assay burden but must respect method-specific limits to avoid dilution below LOD [21,23]. Pre-analytical hygiene (workflow separation, barcoded chain of custody, inhibitor-aware extraction) and internal amplification controls are essential to minimize false negatives [266]. Where infection depth is suspected, reflex testing with viability PCR or grow-out assays can de-risk release decisions [267,268]. A two-tier pipeline—multiplex qPCR prescreens with IACs and guard bands, followed by targeted grow-out confirmation—balances sensitivity, speed, and biological relevance [266,267,268].

12.3. Fungal Pathogens: Fusarium spp. in Cereals and Vegetables

Seedborne Fusarium presents dual challenges: latent infection and persistence of non-viable DNA after treatment. Viability-linked PCR using propidium monoazide (PMA-qPCR) discriminates living from dead propagules and achieves detection limits near 82 spores mL⁻¹ in suspension and ≈91 spores g⁻¹ in soil/plant matrices [269]. Species-resolved EF1α-targeted real-time PCR across eleven Fusarium species correlates strongly with mycotoxin content, enabling surveillance programs to link DNA burden to toxin risk [270]. For harvested onions, pooled-sample PCR (composite subsamples from 50 bulbs, with dual markers for species ID and pathogenicity genes) increased throughput for screening symptomless latent infections [271].

Practical implications: Fungicidal or physical treatments can leave behind amplifiable DNA; PMA-qPCR (or RNA-based approaches) should be prioritized when treatment history may confound risk assessment [269]. Non-destructive imaging (e.g., hyperspectral) can triage large lots rapidly, flagging suspect kernels for molecular confirmation—especially valuable under low prevalence and heterogeneous distribution. Extraction protocols must be matrix-adapted (PVP/BSA additives, bead-based clean-ups) with IACs to detect residual inhibition [91]. For tropical maize seeds, humidity-induced PCR inhibition can be mitigated by pre-washing with 1% PVP.

Integrated Operational Lessons

• Localization drives strategy: ToBRFV contamination is predominantly external; workflows should prioritize surface disinfection and composite RT-qPCR [263,264]. Xcc can occasionally penetrate internal tissues; include viability/grow-out confirmation for borderline results [267,269]. Fusarium often persists as non-viable DNA post-treatment; use PMA-qPCR (or RNA proxies) to align detection with infectivity risk [269].
• Detection ≠ infectivity: Molecular positivity (RT-qPCR/PCR) may reflect residual nucleic acids rather than transmission potential; guard-banding, viability-linked assays, and targeted grow-outs reduce false regulatory actions [263,264].
• Sensitivity at scale: Multiplex qPCR reaching 1:10,000 sensitivity for Xcc sets practical benchmarks; pooling limits and inhibitor control must be validated per method and seed matrix in line with ISTA/EPPO guidance [21,23,266].
• Integrated workflows: Pair high-throughput prescreens (qPCR/RT-qPCR, imaging, metabarcoding) with orthogonal confirmation (viability PCR, grow-out, pathogenicity tests) to balance speed with biological relevance and regulatory confidence [266,267,268,269,270,271,272].

A comparative summary of these operational considerations across diagnostic methods is presented in

Table 4. Concise summary of case studies and practical implications.

Pathogen Class	Commodity/ Target	Primary Diagnostic(s)	Confirmatory/Adjunct	Key Operational Notes
Virus (ToBRFV)	Tomato/pepper seed	RT-qPCR; composite sampling [263,264]	Surface disinfection; grow-out in borderline cases [263]	External contamination; RNA ≠ infectivity; manage inhibition with PVP/BSA [91]
Bacterium (Xcc)	Brassica seed	Multiplex qPCR with IACs (≈1:10,000 sensitivity) [267]	Viability PCR/grow-out; validated disinfection (3% H2O2, 30 min) [261,262]	Respect pooling limits (ISTA/EPPO) [21,23]; reflex rules for suspected internal infection [268]
Fungus (Fusarium spp.)	Cereals, onion	Species-resolved qPCR; pooled-sample PCR [268,269]	PMA-qPCR; hyperspectral triage [263]	Address latent infection; matrix-adapted extraction; humidity mitigation with 1% PVP [91]

.

13. Data Standards and Traceability

Modern seed health testing increasingly relies on digital infrastructure to manage diagnostic data, certification records, and phytosanitary documentation. As laboratories adopt high-throughput molecular and imaging systems, data volumes grow rapidly and, without standardized frameworks, become siloed—diminishing regulatory and commercial traceability and hindering cross-lab comparability [272]. The FAIR principles—Findable, Accessible, Interoperable, Reusable—therefore serve as the cornerstone of digital seed testing systems, promoting consistent data practices, international exchange, and transparency across the seed value chain [273]. In parallel, formal traceability mechanisms (audit trails, version-controlled records, and digital certificates) are mandated under phytosanitary frameworks such as the International Plant Protection Convention (IPPC) and the OECD Seed Schemes, to create verifiable links between analytical results, seed lot identity, and export certification—strengthening biosecurity and market confidence [274].

In practice, applying FAIR to seed diagnostics means structuring information for machine-readability and long-term reuse [272]. Data must be findable, with persistent identifiers (e.g., DOIs, UUIDs) and rich, queryable metadata describing samples, tests, organisms, and context; accessible, meaning it is retrievable via standardized protocols (HTTP, API) under clearly defined access conditions; interoperable, meaning it uses shared vocabularies and schemas (Darwin Core, MIAPPE, ISO XML) to enable cross-platform analysis; and reusable, with explicit provenance, licensing, and methodological detail to support replication and audit [273]. When FAIR is implemented end-to-end, test results and metadata flow cleanly from LIMS into regulatory platforms such as TRACES and ePhyto, eliminating manual re-entry and reducing transcription errors [275].

To achieve that interoperability, laboratories should anchor their data models in established domain schemas. MIAPPE provides the phenotyping-oriented metadata needed to describe plant material, growth conditions, and experimental protocols—fields that, in seed testing, cover lot origin, assay type, and environmental parameters [276]. Darwin Core contributes standardized taxonomy, collection, and occurrence terms that are directly applicable to pathogen–host records and seed-associated detections [277]. For data exchange and certification packaging, XML and JSON are the dominant formats, with XML remaining prevalent in government systems due to mature tooling and long-standing compatibility with phytosanitary databases [275]. Where location matters—e.g., production zones, inspection points, and movement routes—ISO 19115/19139 geospatial metadata standards support precise mapping and traceability [39]. Collectively, these frameworks provide the semantic and structural backbone for global seed health data exchange.

Robust audit trails and chain-of-custody controls are essential for accreditation and trust. Every transaction—from sampling and extraction to analysis and reporting—should be time-stamped, digitally signed, and linked to authenticated user credentials to guarantee authenticity and accountability [39]. In accredited seed testing laboratories, LIMS log sample accession, reagent batches, instrument calibration, analyst actions, and approval steps to demonstrate compliance with ISO/IEC 17025 requirements for traceability and technical competence [39]. At the regulatory level, the international seed trade depends on compliance with frameworks that mandate verifiable diagnostic histories: IPPC certificates must include origin, treatments, and diagnostic details, and the OECD Seed Schemes explicitly link each certified lot to the testing laboratory and competent authority—an approach increasingly reinforced by electronic documentation and metadata traceability practices [275].

Linking laboratory diagnostics to phytosanitary certification is now predominantly electronic. The IPPC ePhyto Solution enables national plant protection organizations (NPPOs) to issue and exchange digital certificates that embed diagnostic references and movement permissions [278]. Within the European Union, TRACES NT records plant product movements and allows authorized users to attach laboratory test reports directly to shipment records, ensuring transparent, audit-ready documentation [279]. In the United States, PCIT (USDA APHIS) ties issued export certificates to laboratory results and origin data, providing end-to-end traceability back to the seed lot [280]. Taken together, these systems reflect a global shift toward electronic phytosanitary certification, which reduces fraud, streamlines verification, and speeds up cross-border movement while preserving detailed diagnostic provenance [275,279,281,282].

Exploratory pilots have evaluated blockchain/distributed ledgers for certificate immutability by storing hashed identifiers of diagnostic results, enabling tamper-proof verification without exposing confidential content. However, broad deployment faces practical hurdles in governance, scalability, cost, and regulator acceptance; most authorities favor hybrid architectures in which conventional databases manage day-to-day operations, while distributed ledgers or integrity tokens are used selectively for verification.

Because infrastructure and capacity vary widely, harmonization should be pragmatic. For laboratories—particularly in developing regions—gradual digital transformation emphasizing open standards and incremental interoperability is more sustainable than wholesale platform replacement. Priority actions include adopting common metadata (MIAPPE, Darwin Core); enabling API-based integration between LIMS and national/international systems (ePhyto, TRACES); assigning persistent identifiers (DOIs/UUIDs) to seed lots, assays, and certificates; and building capacity through targeted FAIR training for data managers and phytosanitary officers [275,276]. Where distributed ledger functionality is desired, pursue phased blockchain adoption through pilots before a national rollout. Aligning diagnostic data standards with certification platforms will deliver a transparent, auditable pathway from laboratory testing to international trade.

Table 5. Comparison of traceability approaches for seed health data.

Approach	Integrity (Tamper-Proof)	Scalability	Cost	Regulatory Acceptance
Blockchain	Very high (immutable ledger)	Moderate (requires infrastructure)	High (setup and maintenance)	Emerging (pilot stage, limited global adoption)
XML Certificates	High (digital signatures)	High (widely supported)	Low (standardized, low overheads)	High (accepted by IPPC, OECD, TRACES)
Hybrid API	High (secure API + XML)	Very high (cloud-ready, interoperable)	Moderate (integration costs)	High (preferred for interoperability and ePhyto integration)

summarizes trade-offs among blockchain, XML-based certificates, and hybrid API approaches (integrity, scalability, cost, regulatory acceptance) to guide system selection and sequencing [275,282,283].

14. Ethics, IP, and Adoption

The rapid adoption of molecular, imaging, and artificial intelligence (AI)-based diagnostics in seed health testing raises intertwined questions of data ownership, intellectual property (IP), transparency, and equitable access. Traditional seed testing leaned on open, standardized methods (e.g., ISTA rules), while contemporary platforms increasingly depend on proprietary datasets, algorithms, and patented reagents—creating tension between innovation incentives and the scientific openness needed for trustworthy regulation and global comparability [283,284].

AI-enabled seed diagnostics (e.g., deep learning for hyperspectral imaging, and pathogen classification) rely on large, labeled image and spectral datasets; when these corpora are proprietary, independent benchmarking and reproducibility suffer and risks of hidden bias increase [243]. In regulated settings, transparency is essential: the OECD Principles on AI and FAIR-AI guidance stress that algorithms influencing phytosanitary decisions must be explainable and auditable for accreditation and oversight [284,285,286]. In response, community initiatives advocate open-access agricultural AI repositories—GenBank-style—for standardized metadata (e.g., MIAPPE), interpretable models, reproducible results, and independent performance verification before regulatory acceptance [276].

IP constraints also influence the development and use of molecular assays. Licensing requirements for primers, probes, and isothermal amplification systems are becoming more common because many sequence targets and chemistries fall under diagnostic-use or biotechnology patents. These restrictions determine who is permitted to conduct specific assays or modify them for local applications. In practice, proprietary LAMP panels and probe-based qPCR kits for regulated pathogens may require additional permissions even when the underlying genomic information is publicly available, which limits opportunities for method adaptation and independent validation. International organizations have increasingly emphasized the need for transparent and flexible licensing practices to support equitable access to diagnostic tools, particularly when public research contributes to the creation of these technologies.

Where public sequence databases (GenBank/EMBL) underpin patented diagnostics, “knowledge privatization” concerns arise [287]. One remedy is dual licensing: open, royalty-free use for non-commercial research and teaching, with paid commercial licenses. Coupled with collaborative validation under ISTA/EPPO structures, this approach preserves access while ensuring that licensed assays undergo independent verification rather than relying solely on vendor claims [287].

Regulatory acceptance requires a workable compromise between confidentiality and scientific transparency. Developers may keep trade secret details (e.g., reagent compositions, model weights), yet regulators need enough evidence to evaluate reliability and reproducibility; best practice is tiered transparency that discloses validation data, accuracy metrics, and reference panels without forcing full IP disclosure—consistent with the EU AI Act requirements for explainability and risk controls [288].

Regardless of ownership, external validation remains non-negotiable. Molecular methods must demonstrate sensitivity, specificity, LoD, repeatability, and reproducibility under defined conditions; AI systems should be tested on independent, well-curated datasets that capture variation across seed species, lots, imaging conditions, and pathogen spectra. Expanding EURL-style, multi-lab networks for ring trials and proficiency testing would provide standardized quality assurance frameworks for both molecular and AI-enabled diagnostics and accelerate regulatory convergence [289].

Sustainable adoption also depends on people and process. Transitioning labs need competency in bioinformatics, data stewardship, and biosafety; training should cover technical analytics and ethical use policies (data handling, consent, security) [290]. Capacity-building under IPPC and OECD programs emphasizes competency-based training and harmonized documentation so that diverse laboratories can meet the same quality bar and integrate with national and international certification systems [288].

These technologies introduce biosafety and dual-use considerations. Synthetic biology workflows and high-resolution pathogen data can be misapplied; AI-driven prediction tools might inadvertently expose vulnerabilities in agricultural systems. Ethical deployment therefore requires governance that limits function creep (e.g., non-agricultural surveillance, trade manipulation), enforces role-based access, and documents acceptable-use constraints.

Finally, integrating diagnostic outputs into bio-surveillance networks can strengthen early warning for transboundary pests and pathogens while respecting data sovereignty and privacy; agreements should specify data flows, retention, and re-use, and follow FAIR and CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics) so that communities contributing data retain agency and benefit from derived insights.

Outlook: Ethics and IP will shape the pace and equity of diagnostic innovation in seed systems. Progress hinges on transparent validation pipelines; fair, flexible licensing for primers/probes and software; open, well-documented AI datasets; robust multi-lab proficiency testing; and bio-surveillance governance that protects both security and openness. With these guardrails, molecular, imaging, and AI platforms can scale responsibly improving biosecurity and trade while safeguarding scientific integrity and global accessibility

Example of a Three-Year Roadmap for Laboratory Implementation:

• Phase 1 (Year 1): validate LAMP assays for three priority pathogens and establish baseline performance metrics under ISO 17025 guidelines.
• Phase 2 (Year 2): integrate AI-driven imaging workflows with explainability tools (Gradient-weighted Class Activation Mapping (Grad-CAM), SHapley Additive exPlanations (SHAP)) and link them to Laboratory Information Management Systems (LIMS) for traceability.
• Phase 3 (Year 3): Achieve full ISO 17025 accreditation for multi-modal workflows combining molecular, imaging, and AI diagnostics, supported by proficiency testing and FAIR-compliant data management.

For the transition to best practice, start with pilot targets, expand gradually, retain legacy assays during evaluation, and maintain comprehensive records under FAIR data principles, while continuously training personnel in molecular and bioinformatics competencies. Day-to-day execution should still meet practitioner essentials: validate methods to ISO 17025 (sensitivity, specificity, repeatability, reproducibility) [39]; enforce full traceability via LIMS with audit trails [279 manage data under FAIR/MIAPPE/Darwin Core [272,278]; confirm reagent/software licensing and AI transparency [274]; record personnel competency for regulatory programs [274]; and embed sustainability measures to minimize waste and energy use.

Method selection should follow pathogen biology, seed matrix, and regulatory requirements (see

Table 6. Method selection framework by pathogen class and objective.

Pathogen Class	Primary Diagnostic Approach	Complementary/ Confirmatory Methods	Key Considerations for Selection
Fungi	Blotter or agar plate assays with qPCR or LAMP	X-ray or hyperspectral imaging for internal infection mapping	Combine morphological and molecular results; use viability PCR for treated or dormant infections
Bacteria	qPCR or dPCR targeting species-specific genes (267)	Grow-out or pathogenicity assays for viability	Include internal amplification controls; validate detection limits in ring tests
Viruses/Viroids	RT-qPCR or RT-LAMP [291,292]	ELISA or LFIA [79]	Use validated primers and antibodies; confirm genotype inclusivity
Oomycetes/Nematodes	LAMP or metabarcoding panels [243]	Microscopic or imaging confirmation	Optimize extraction for inhibitory seed matrices; assess recovery efficiency
Mixed/Unknown Agents	Amplicon or shotgun metagenomics [243]	Culture or imaging for viability confirmation	Apply contamination control; incorporate synthetic spike-ins and negative controls

).

In brief: fungi often benefit from blotter/agar plate assays paired with qPCR or LAMP, with X-ray or hyperspectral imaging for internal infection mapping and viability PCR where treatments obscure infectivity; bacteria are best served by qPCR/dPCR targeting species-specific genes (267) with grow-out or pathogenicity tests for viability and strict use of IACs and ring-test LoD validation; viruses/viroids prioritize RT-qPCR or RT-LAMP [79] and can be complemented by ELISA or LFIA [79] with validated inclusivity; oomycetes/nematodes leverage LAMP or metabarcoding panels plus microscopic/imaging confirmation, optimizing extraction for inhibitory matrices; and mixed or unknown agents call for amplicon or shotgun metagenomics [243] with culture/imaging to confirm viability and rigorous contamination controls.

Operationalization benefits from a staged implementation roadmap (see

Table 7. Implementation roadmap for diagnostic laboratories.

Stage	Goal	Key Activities	Outputs/Metrics
Pilot Phase	Demonstrate feasibility and build baseline metrics	Select key pathogens; compare new assays with reference methods; assess readiness	Preliminary LOD and cost-per-test benchmarks
Validation Phase	Analytical and diagnostic validation	Conduct inter-laboratory trials and robustness testing	Validated SOPs and ISO/ISTA documentation
Routine Use Phase	Establish accredited workflows and quality control	Integrate methods into LIMS; ensure full digital traceability [279]	Continuous proficiency data, audit readiness

).

A Pilot Phase establishes feasibility and baseline metrics (e.g., preliminary LoD and cost-per-test); a Validation Phase executes inter-laboratory trials and robustness testing to produce validated SOPs and ISO/ISTA documents; and a Routine Use Phase embeds accredited workflows with LIMS-based traceability and ongoing proficiency/audit readiness [39,279]. Surveillance programs favor field-deployable assays (LAMP/LFIA), quarantine and certification rely on ISO 17025-validated qPCR and grow-out [39], research leverages metagenomics and AI-assisted phenotyping for broad-spectrum discovery [95], and routine industry testing scales with multiplex qPCR integrated with imaging for throughput and reproducibility [267].

15. Conclusions

Seed health diagnostics are entering a new era in which the boundaries between molecular biology, imaging, automation, and artificial intelligence continue to dissolve. What was once a predominantly manual and culture-based process has transformed into a multidimensional, data-rich ecosystem where accuracy, reproducibility, and traceability are as critical as the detection event itself. Modern workflows increasingly draw strength from integration rather than replacement: classical assays remain indispensable anchors for viability and biological relevance, while molecular platforms, next-generation sequencing, non-destructive imaging, and machine learning models provide unprecedented speed, sensitivity, and analytical depth.

The future of seed health assurance will depend on laboratories’ ability to harmonize these diverse tools within robust quality and accreditation frameworks. Standardized validation, transparent data governance, explainable AI, and digital traceability will form the backbone of credible diagnostics capable of supporting international seed movement, regulatory decision-making, and biosecurity. At the same time, the sector must retain biological realism, ensuring that computational precision does not obscure the ecological and epidemiological complexity of seedborne pathogens.

As diagnostic pipelines scale, networks of interoperable laboratories will become essential for consistent global surveillance, proficiency testing, and cross-border verification. Advances in digital infrastructure, from FAIR aligned data management to LIMS integrated audit trails, will strengthen confidence in diagnostic outputs. Ethical stewardship, including responsible use of AI, equitable access to technologies, and respect for data sovereignty, will be equally important in shaping how innovations are adopted worldwide.

Ultimately, next-generation seed health systems will be defined by their ability to blend scientific rigor with technological agility. Laboratories that integrate validated molecular and imaging platforms, transparent analytics, and high-confidence data pipelines will lead the way in protecting agricultural productivity, sustaining biodiversity, and enabling the safe and efficient global exchange of seeds. Looking forward, future research should focus on developing viability-aware molecular assays, creating multi-modal diagnostic platforms that fuse imaging, molecular data, and AI, and designing high-precision explainable AI models suitable for regulatory use. Additional priorities include building curated genomic and spectral reference libraries, advancing amplification-free CRISPR diagnostics, and deepening understanding of how imaging signatures reflect underlying seed physiology. Continued progress will also depend on adaptive sampling frameworks, harmonized validation pathways, sustainable and low-resource diagnostic technologies, privacy-preserving data infrastructure, and ethical governance models that balance transparency with intellectual property considerations. Together, these directions will support the development of resilient and globally interoperable diagnostic systems capable of meeting the growing complexity of modern seed health challenges.