Search Results (4,937)

Cereal production is increasingly constrained by soil degradation, water scarcity, climate variability, and rising disease and weed pressure. This review synthesizes current knowledge on the role of regenerative agronomic practices in cereal production, with particular emphasis on soil health, plant disease management, water-use efficiency, and yield stability. Available evidence consistently indicates that the greatest benefits arise not from individual practices but from integrated systems combining reduced tillage, crop residue retention, diversified crop rotations including legumes, cover crops, organic fertilization, and biologically based pest management. Such practices can increase soil biological activity and its ability to limit disease by enriching functionally beneficial microbial communities and limiting pathogens through competition for resources and niches, antibiosis, hyperparasitism, and the induction of plant resistance. They can also improve soil structure, water infiltration, water retention, and crop resilience to drought stress and, under certain conditions, reduce erosion, nutrient losses, and yield variability. However, the effects of regenerative practices are strongly dependent on soil type, climate, nitrogen balance, pest pressure, and the extent of adoption of regenerative practices. Risks may arise during the transition period, including yield declines, nitrogen immobilization, weed infestation, and increased disease pressure. Evaluation of these systems should encompass not only yield but also the grain quality and phytosanitary status, soil organic carbon stocks throughout the soil profile, N₂O emissions, and production profitability. The review covers cereal systems from temperate, humid, arid and semi-arid zones, and the results were interpreted considering climate, soil quality, water availability, and agronomic practices, as the same practice can produce different effects in different agroecological zones. Further research should prioritize long-term, multifactorial experiments conducted across diverse agroecological environments that integrate agronomic performance, environmental sustainability, and crop quality. Full article

Plant diseases impose major constraints on global crop productivity and pose a major threat to food security. Here, we review transcription factors (TFs) as central orchestrators of plant defense, consolidating recent advances in how these regulators connect pathogen perception to immune signaling, transcriptional reprogramming, and durable defense responses. Initially, we combined a literature-based synthesis with a natural language processing (NLP) analysis of 1647 PubMed abstracts published between 2021 and 2026 to map dominant and underexplored TF families associated with plant immunity. WRKY, MYB, AP2/ERF, bHLH/MYC, and NAC dominated the recent literature, whereas families such as NF-Y, Trihelix, PLATZ, TCP, and GRAS represent emerging regulatory actors. Across these and other families, TFs integrate pattern- and effector-triggered immunity, hormone crosstalk, chromatin dynamics, non-coding RNA regulation, post-translational modifications, and metabolic remodeling, in addition to cell-type-specific expression. Further evidence indicates that pathogens frequently manipulate TFs to weaken host defense, underscoring their central position in plant molecular physiology and plant-pathogen coevolution. The data emphasize that TF function is context-dependent and influenced by multilayered regulation, cell type, pathogen lifestyle, and host genetic background. This review provides a framework for understanding TFs in plant immune control and highlights TF-centered strategies for engineering durable crop resistance, along with future challenges. Full article

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has emerged as a promising tool for monitoring crop physiological status and supporting precision disease management. However, the capacity of multispectral vegetation indices to detect foliar diseases under commercial field conditions remains insufficiently understood, particularly in perennial crops grown in humid tropical environments. This study evaluated the potential of UAV-derived multispectral vegetation indices to assess the physiological response of avocado (Persea americana Mill.) canopies affected by anthracnose caused by Colletotrichum fructicola in Amazonas, Peru. Disease incidence and severity were assessed through field evaluations, while multispectral imagery was acquired using a UAV equipped with a MicaSense RedEdge-MX Dual sensor. Vegetation indices including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Red Edge Chlorophyll Index (CIred-edge), Plant Senescence Reflectance Index (PSRI), and Visible Atmospherically Resistant Index (VARI) were calculated, and their relationships with anthracnose incidence were analyzed using Spearman’s rank correlation. Field observations confirmed a high incidence of foliar anthracnose with a heterogeneous spatial distribution across the orchard. Multispectral imagery successfully characterized spatial variability in the canopy physiological condition, revealing differences in vegetation vigor, chlorophyll-related reflectance, and senescence among experimental blocks. Nevertheless, none of the evaluated vegetation indices showed statistically significant correlations with anthracnose incidence (p > 0.05), indicating that spectral variability primarily reflected the general canopy physiological status rather than a disease-specific spectral response. These findings demonstrate that UAV-derived multispectral vegetation indices are valuable for monitoring spatial variability in the avocado canopy condition but have limited capability for independently detecting anthracnose under humid tropical field conditions. Future studies integrating multi-temporal UAV acquisitions, hyperspectral and thermal imagery, LiDAR, environmental variables, and machine-learning approaches are expected to improve the early detection and spatial prediction of anthracnose in avocado production systems. Full article

Plant anthracnose, caused by Colletotrichum spp., is a class of significant diseases that severely threatens global crop production. Traditional management strategies, including chemical control, are hindered by inherent limitations such as the frequent emergence of pathogen resistance, low pesticide utilization efficiency, high environmental residue risks, and inconsistent field efficacy. Moreover, achieving further improvements in control efficacy is constrained by the complex biological characteristics of Colletotrichum species, particularly their hemibiotrophic lifestyle and latent infection strategies. In recent years, nanotechnology has emerged as a potential approach for the management of anthracnose. This review summarizes the research progress of various nanomaterials in the control of plant anthracnose. It analyzes the proposed multi-mechanism modes of action of nanomaterials tailored to the specific infection traits of Colletotrichum. Particular emphasis is placed on analyzing the theoretical mechanisms and preliminary in vitro evidence of nano-enabled controlled-release systems in addressing asymptomatic latent infections and achieving targeted, precise delivery. However, it must be emphasized that most current findings are derived from laboratory or controlled-environment studies, and the actual field-scale effectiveness, long-term environmental behavior, and multi-trophic safety of many nanomaterials remain insufficiently confirmed. Building upon these insights, this review thoroughly evaluates the critical challenges facing the agricultural field application of nanomaterials, such as ecotoxicity, formulation stability, scalable industrial production, and regulatory gaps. This review aims to provide objective theoretical support and technical references for achieving safe, efficient, and sustainable nano-enabled green management of plant anthracnose. Full article

Greenhouses in subtropical and tropical regions must be designed as agricultural engineering systems adapted to local climates, rather than simply replicating the “insulation” models of temperate areas. Under extreme climatic conditions such as persistent high temperatures, intense solar radiation, high humidity, heavy rainfall, and frequent extreme winds, greenhouses transform from enclosed insulation layers into selective climate filters, mitigating crop stress while maintaining close contact with the outdoor environment. This paper summarizes how these climate drivers are reshaping the use, structure, and control concepts of greenhouses, emphasizing that the performance of warm-zone greenhouses depends primarily on heat dissipation, humidity management, and biohazard control, rather than heating and insulation. In this review, we analyze the climatic boundary conditions that define warm-climate conservation cultivation, including long-term overheating risk, high UV radiation, vapor pressure deficit, and suppressed condensation tendency, as well as storm-induced uplift and dynamic loads. These constraints necessitate unique structural forms: tall, lightweight, well-ventilated building types with large roof and side openings, roof geometries that facilitate rainwater runoff, sophisticated drainage systems, and corrosion-resistant materials suitable for humid and coastal environments. Because insect netting significantly reduces ventilation, pest control and temperature regulation become co-design issues, requiring oversized vents, optimized airflow paths, and hybrid roof–mesh structures. Ventilation is considered the primary climate-control mechanism, supplemented by passive cooling measures such as shading and radiation/optical management (e.g., diffuse films and near-infrared-selective films). Active evaporative cooling is considered a conditional measure due to humidity limitations and disease risks. This paper also integrates the impacts on specific crops (fruits and vegetables, leafy greens, and orchids). It highlights emerging trends: typhoon-resistant and adaptive geometries, computational fluid dynamics (CFD)-based design, and sensor-rich IoT/digital twin control frameworks. These principles collectively establish a coherent design framework for achieving resilient, resource-efficient greenhouse production in warm climates. Full article

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

Plants have evolved sophisticated immune systems to defend against diverse pathogens, whereas pathogens deploy effectors to manipulate host cellular processes for successful infection. Recent studies have revealed that pathogen effectors can target host RNA processing, particularly pre-mRNA alternative splicing (AS), to reshape transcript isoform profiles and modulate plant immunity. However, the common principles and specific differences among pathogen effector-mediated regulation of host AS and their impacts on plant immunity have not been systematically summarized. In this review, we summarize recent advances in pathogen effector-mediated regulation of plant AS, focusing on three major mechanisms: targeting host splicing factors, altering RNA regulatory elements, and interfering with RNA-processing pathways. We discuss how effector-induced AS reprogramming affects immune-related gene expression and contributes to pathogen virulence. Furthermore, we highlight the emerging potential of AS regulation in disease resistance and disease-resistant crop breeding, and propose future directions for dissecting effector-specific splicing targets and translating AS-based regulatory mechanisms into crop improvement strategies. This review emphasizes host RNA splicing as an important regulatory layer in plant-pathogen interactions and provides new perspectives for understanding pathogen manipulation of plant immunity. Full article

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

Rice leaf diseases significantly affect crop health and yield potential, creating a need for accurate and timely disease diagnosis. Many existing approaches still rely on single-stream feature extraction architectures, which may limit the ability to simultaneously capture global contextual information and fine-grained disease characteristics. Moreover, limited interpretability and decision-support capability hinder their practical deployment in real-world rice farming. To address these limitations, we employed a framework consists of two parallel feature extraction streams designed to capture different characteristics of disease patterns. The first stream uses a Swin Transformer to learn global contextual information and long-range spatial relationships across the leaf image. The second stream employs ConvNeXt to extract local texture features, including lesion details, spots, and color variations. By combining these complementary representations, the proposed framework effectively integrates global semantic information with local disease-specific features for improved classification performance. The extracted features from the two streams are fused through concatenation followed by an attention-based feature refinement module, enabling adaptive weighting of discriminative features. The refined representation is then used by a fully connected classifier for disease prediction. To enhance model interpretability, Grad-CAM visualization is incorporated to highlight disease-relevant regions and provide visual explanations for the model decisions. Furthermore, an LLM-based advisory module is integrated as a post-diagnosis decision-support component to provide contextualized disease management information and suggestions based on the predicted disease category. The generated suggestions are intended to support, rather than replace, expert agronomic recommendations and should be validated by agricultural professionals before practical application. The proposed framework was evaluated on two rice leaf disease datasets, achieving accuracies of 99.55% and 97.06% on Dataset-1 and Dataset-2, respectively, which are higher than those reported in previous studies. Additionally, five-fold cross-validation on Dataset-1 achieved an average accuracy of 98.91% ± 0.47, demonstrating the stability of the proposed approach. Cross-dataset evaluation using nine common disease classes across both datasets achieved 90.80% accuracy, indicating improved generalization across different data distributions. The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications. Full article

Immune-mediated inflammatory diseases (IMIDs) arise from interactions among genetic susceptibility, epithelial barrier function, microbiota, diet, and other environmental exposures. Modern diets influence mucosal immunity not only through fibre intake, food processing, and microbiota composition, but also through a less explored exposure layer: plant-derived molecules with potential immune activity. Crop breeding, intensive agriculture, global trade, gluten-free substitutes, and plant-based food technologies have changed the spectrum, dose, concentration, and matrix in which plant defence proteins, antinutritional factors, endogenous toxicants, and novel plant antigens reach the intestinal surface. In this structured, hypothesis-generating narrative review, we propose dietary adjuvanticity as a mechanistic framework for considering how selected food-derived molecules may amplify mucosal immune responsiveness, modify antigen presentation, disturb barrier function, or lower tolerance thresholds without necessarily acting as classical autoantigens. The framework differs from general food-derived immunomodulation, nutritional exposomics, and diet-microbiota-host interaction models by focusing specifically on adjuvant-like immune amplification at the intestinal mucosa. The ASIA concept is used only in Shoenfeld’s functional sense, as an analogy for exogenous immune amplification through innate activation, danger signalling, bystander activation, epitope spreading, and loss of tolerance in susceptible hosts; it is not applied as a dietary diagnosis. Wheat amylase-trypsin inhibitors, gluten epitopes, lectins, potato glycoalkaloids, saponins, quinoa prolamins, emerging legume proteins, L-canavanine, and tolerance-promoting plant substrates are discussed with explicit separation of established clinical evidence, strong mechanistic evidence, preclinical/ex vivo evidence, and speculative disease-modifier hypotheses. Overall, plant-derived exposures are best interpreted as potential modifiers within the IMID exposome, not as primary causes of autoimmunity. Testing this model will require defined exposures, food-matrix and processing studies, biomarkers of barrier and immune activation, patient stratification, and controlled human studies. Full article

Durian (Durio zibethinus L.) is a high-value tropical fruit crop that has recently been cultivated on a commercial scale in Hainan Province, China. However, emerging diseases increasingly threaten sustainable production and fruit quality. Field surveys conducted in Sanya, Hainan, identified stem rot as the predominant disease affecting durian trees, with a disease incidence of approximately 26.6%. Affected trees exhibited necrotic lesions at stem-branch junctions, followed by leaf abscission and progressive decline. The causal agent was identified using morphological characteristics, phylogenetic analyses of the internal transcribed spacer (ITS) and translation elongation factor 1-alpha (TEF-1α) and RNA polymerase II second largest subunit (RPB2) gene regions, and pathogenicity assays. Inoculated plants developed symptoms consistent with those observed under field conditions, thereby fulfilling Koch’s postulates. Both morphological and multilocus molecular analyses consistently identified the pathogen as Fusarium solani. A total of 20 Fusarium isolates were obtained from diseased durian stem samples. To our knowledge, this is the first report of F. solani causing stem rot of durian in Hainan Province, China. In addition, Bacillus subtilis SYST2 exhibited strong antagonistic activity against F. solani isolates FS-1, FS-2, and FS-3 in vitro, with inhibition rates of 55.7%, 54.6%, and 53.3%, respectively. Field trials further demonstrated that application of SYST2 significantly suppressed disease development, achieving a biocontrol efficacy of 53.51%. These findings establish F. solani as a causal agent of durian stem rot in Hainan and highlight the potential of Bacillus-based biological control as a sustainable strategy for disease management in durian production systems. Full article

This article presents a systematic, evidence-weighted review of Deep Learning (DL) in farming, with a primary emphasis on the agricultural production stage and on four operational domains: precision crop management, precision livestock farming, soil and water resource management, and autonomous agricultural systems. Following a PRISMA 2020-oriented protocol, 56 primary studies (42 with quantitative results) were retained from an initial pool of 18,731 records. The reviewed literature reports applications in plant disease detection, weed recognition, yield prediction, fruit detection, livestock identification and health monitoring, soil-property estimation, crop-water-stress assessment, and robotic perception. High performance on controlled datasets, however, is frequently reported without external, temporal, or cross-site validation, making practical generalisation difficult to establish: in one widely cited benchmark, disease-classification accuracy fell from above 99% on held-out laboratory images to 31.4% on field-acquired images of the same classes. Persistent weaknesses include the limited availability of public benchmarks, inconsistent validation protocols, limited interpretability, fragmented data governance, and insufficient techno-economic analysis. The review argues that the next stage of agricultural AI should be judged less by isolated benchmark scores and more by field realism, reproducibility, deployment maturity, and practical usefulness. Unlike broad surveys that mainly catalogue architectures and applications, this review interprets the literature according to dataset representativeness, validation protocols, benchmarking transparency, deployment realism, and reproducibility, distinguishing algorithmic performance under controlled conditions from practical readiness for real farming environments. The most promising research directions include self-supervised and multimodal learning, explainable and privacy-preserving AI, edge-aware deployment, and hybrid process-informed models. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

Mulberry (Morus alba L.) is an important perennial woody crop for sericulture, medicinal resource development, and ecological conservation. Accurate disease and pest detection in natural mulberry orchards remains challenging due to dense foliage, severe occlusion, varying illumination, and the presence of small lesions with weak texture features. To address these problems, this study proposes CLH-DETR, a lightweight detection framework improved from the Real-Time Detection Transformer (RT-DETR) for mulberry disease and pest detection under natural field conditions. The proposed model introduces four targeted improvements: Cross-Stage Partial Progressive Multi-Scale Feature Aggregation (CSP-PMSFA) is designed to strengthen multi-scale lesion feature extraction while reducing redundant computation, the Lesion Detail Enhancement Block (LDEB) is designed to enhance weak lesion details and responses related to lesion boundaries, Haar Wavelet Downsampling (HWD) is adopted to preserve texture and structural information during feature downsampling, and Focaler-ShapeIoU is introduced to improve bounding-box regression for irregular disease regions. Experiments on the Mulberry Disease Dataset show that CLH-DETR achieves 80.1% precision, 75.3% $mA{P}_{50}$, and 55.9% $mA{P}_{50:95}$, improving the RT-DETR baseline by 3.9, 2.3, and 2.2 percentage points, respectively. Meanwhile, the number of parameters decreases from 19.9 M to 13.4 M, and the computational cost is reduced from 57.1 G to 46.4 G FLOPs. Compared with representative YOLO- and DETR-based detectors, CLH-DETR provides a favorable balance between detection accuracy and model complexity. When deployed on an iPhone 16 Pro using CoreML with FP16 precision, the model achieved an average latency of 16.5 ms per image, corresponding to 60.6 FPS. This result indicates its potential for edge-assisted mulberry disease inspection under the tested hardware conditions. Full article

Livestock production in Africa occurs across highly heterogeneous agroecological and management environments, ranging from extensive pastoral and mixed crop–livestock systems to intensive enterprises. These systems are characterized by seasonal and spatial variation in feed resources, reliance on locally available forage and agricultural by-products, climatic stress, endemic diseases, and the use of indigenous and locally adapted breeds. Such conditions create distinctive microbiome–host interactions that remain poorly represented in global livestock omics research. Although the gut microbiome is central to nutrient utilization, immune function, metabolic homeostasis, and resilience, the functional mechanisms linking microbial communities, diet, host physiology, and productivity in African livestock remain insufficiently characterized. African systems are particularly underrepresented in integrated microbiome–metabolomics datasets, longitudinal studies, and artificial intelligence (AI)-enabled predictive models, limiting the development of context-specific precision nutrition strategies. This review examines the integration of metabolomics and AI with microbiome and host data to advance precision livestock nutrition within an African and One Health context. It identifies both substantial constraints and strategic opportunities. Limited research infrastructure, high-quality regional datasets, computational capacity, and specialized expertise remain major barriers. Conversely, Africa’s diversity of livestock breeds, feed resources, agroecological conditions, and naturally occurring resilience phenotypes provides an important opportunity to identify microbiome–metabolite signatures associated with feed efficiency, disease resilience, climate adaptation, and product quality. Emerging metabolomics and computational capacity, particularly in South Africa, could support regional research networks and continental data infrastructures. Furthermore, the review proposes an Africa-specific approach that develops locally grounded, scalable, and resource-sensitive precision nutrition strategies, strengthening antimicrobial stewardship, animal health, food safety, climate resilience, sustainable livestock production, and broader One Health objectives. Full article