Search Results (1,464)

The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

Plant-growth-promoting microorganisms (PGPMs) have emerged as important biological tools for improving plant nutrition, crop productivity, soil health, and resilience to environmental stresses, contributing to more resource-efficient agricultural systems. This review provides a comprehensive overview of recent advances in PGPM research, with particular emphasis on next-generation microbial technologies, including synthetic microbial communities, advanced bioformulations, omics-based approaches, precision agriculture, and their contribution to the circular bioeconomy. This review combines a narrative synthesis with a bibliometric analysis based on a dataset of 801 English-language articles and review papers retrieved from the Web of Science Core Collection on 22 May 2026. Bibliometric mapping identified a rapidly expanding and increasingly interdisciplinary research landscape centered on microbial biostimulants, biofertilizers, circular economy, soil health, microbiome engineering, and climate-smart agriculture. This narrative synthesis highlights that advances in multi-omic technologies, microbiome research, and formulation strategies are improving our understanding of plant–microbe interactions and supporting the development of more targeted microbial products. At the same time, widespread agricultural implementation remains constrained by inconsistent field performance, limited ecological validation, formulation challenges, fragmented regulatory frameworks, and insufficient long-term biosafety assessments. Future research should prioritize long-term field validation across diverse agroecosystems, standardized efficacy and safety evaluation, integration of artificial intelligence with multi-omics datasets, development of ecologically reliable microbial consortia, and regulatory harmonization to facilitate responsible commercialization. Full article

The stability and reliability of temperature sensors in industrial cooling systems are critical to process quality, energy efficiency, and operational safety. However, existing approaches lack systematic stability metrics and intelligent predictive capabilities. This study proposes an AI-driven framework integrating stability feature engineering with machine learning models for fault identification and early prediction of temperature sensors in power plant cooling systems. The framework introduces three physics-based stability indicators—rolling standard deviation (σ_roll), variation intensity index (VII), and short-term variation magnitude (ΔT_short)—to quantify sensor signal quality. These features, combined with operational parameters, are used to train support vector machine (SVM) and Long Short-Term Memory (LSTM) models for binary classification. The framework is validated using over 260,000 one-minute records per unit collected from three parallel steam-turbine generating units (Units 1, 2, and 3) of the same coastal thermal power plant. Each unit is served by an independent once-through seawater cooling loop instrumented with redundant Pt-100 temperature sensors at the inlet and outlet manifolds; the three units differ in their operating profile—Unit 1 operates under variable load with frequent cold-start events, Unit 2 under moderate variable load, and Unit 3 under stable high-load conditions—with data collected at 1 min intervals from January to June 2025. Under an explicitly anomaly-positive evaluation, with the full confusion matrix reported for every unit and model, classification performance is limited and strongly unit-dependent. In real-time identification, AUC-based ranking ability varies across units (SVM AUC = 0.65, 0.75, and 0.98 for Units 1–3; LSTM AUC = 0.66, 0.31, and 0.52), but under the extreme class imbalance (anomaly rate ≈ 0.07–0.13% in the test partitions), the calibrated operating-point precision and F1-scores remain low for all unit–model combinations (F1 ≤ 0.26, MCC ≤ 0.28). McNemar’s test indicates statistically significant paired differences for Units 1 and 2 but not for Unit 3. These results show that, on this dataset, neither model attains reliable anomaly classification, and that all reported metrics must be interpreted together with the disclosed confusion-matrix counts and severe class imbalance. The primary contribution of the framework is therefore methodological—physics-based stability indicators, redundant sensor cross-checking, and an operational false-alarm analysis—rather than high-accuracy prediction, and the study highlights the difficulty of learning-based prediction for rare, rule-defined thermal sensor anomalies. Full article

Labor shortages and the push for higher greenhouse efficiency have accelerated interest in automated harvesting. However, the development of a universal harvesting robot has been constrained by large variations in crop architecture, especially plant height. In this review, a height-based stratification of greenhouse harvesting robots and transferable high-wire crop harvesters is presented, covering ground-level crops (<0.6 m, e.g., strawberry), medium-height crops (0.6–1.5 m, e.g., tomato), and high-wire crops (>1.5 m, e.g., trellised cucumber). For each height layer, key design features, technical progress, prototype performance, and common obstacles—including fruit occlusion, mechanical crop damage, unreliable operation, and high commercial costs—are analyzed. Future efforts should target intelligent perception, soft end-effectors, and height-specific solutions (swarm robotics for ground crops, modular hybrid designs for medium crops, infrastructure co-design for high-wire crops). By using plant height as the primary stratification criterion, a design-oriented framework is provided, distinct from conventional crop-type or mechanism-based categorizations. Full article

Brassica crops, encompassing globally important vegetables and oilseeds, face severe threats from diverse biotic and abiotic stresses. Plant secondary metabolites constitute the chemical foundation of defence, and metabolomics has emerged as an effective systems biology tool for comprehensively dissecting stress-induced metabolic changes. Recent progress in applying metabolomics to elucidate defence mechanisms in Brassica crops is systematically synthesised here. Major stresses confronting Brassica crop production and the metabolic basis of plant defence are first outlined. Current analytical platforms, including liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, ion mobility spectrometry, and mass spectrometry imaging, are critically evaluated alongside data processing workflows and multi-omics integration strategies. Key defence-related metabolite classes identified in Brassica crops, notably glucosinolates (GSLs) and their hydrolysis products, phenolic compounds, and lipid-derived signalling molecules, are surveyed with emphasis on their respective functions in biotic and abiotic stress responses. Metabolomics has been instrumental in revealing distinct metabolic reprogramming patterns triggered by diverse stresses, including pathogen infection, insect herbivory, drought, salinity, temperature extremes, and heavy metal stress. Metabolomics-informed crop improvement strategies, including marker-assisted breeding, genetic and metabolic engineering, and precision agronomic practices, are discussed together with current technical bottlenecks and future directions involving artificial intelligence, metabolic modelling, and spatial metabolomics. The compiled knowledge provides a comprehensive reference for leveraging metabolomics to enhance stress resilience and sustainable production of Brassica crops. Full article

Abiotic stresses—drought, salinity, extreme temperature, flooding, and heavy-metal toxicity—constrain rice (Oryza sativa L.) yield worldwide, and the cellular programmes underlying them are unevenly distributed across cell types that bulk-tissue assays average together. This review examines, from a methodological standpoint, what single-cell and spatial omics technologies can and cannot establish about rice abiotic stress biology. We first define the modality space: single-cell omics measures RNA, chromatin accessibility, DNA methylation, protein, or metabolite features at the resolution of individual cells or nuclei, whereas spatial omics measures such features while retaining tissue coordinates; the two are complementary rather than interchangeable. We then treat each platform class—droplet-based scRNA-seq, combinatorial-indexing approaches including SPLiT-seq, nuclei-based snRNA-seq and multiome, sequencing-based and imaging-based spatial transcriptomics—under a common template covering measurement principle, the questions each can answer, applicability to rice tissues, dominant biases, and the inferences each cannot support. To make evidence strength comparable across a heterogeneous literature, we apply a four-tier scheme throughout: Tier A, direct rice cell-resolved or spatial evidence with functional or field validation; Tier B, robust rice functional and localization evidence without single-cell data; Tier C, cell-resolved evidence without causal validation; and Tier D, cross-species analogy or reasoned proposal. Applying this scheme shows that the genes with genuine breeding traction in rice—SUB1A, OsHKT1;5, OsHMA3, OsNRAMP5, DRO1—rest on Tier B evidence from classical genetics and field testing, whereas the most cell-resolved rice evidence concentrates in root outer layers and barrier formation at Tier C, and heat and cold stress, despite dominating yield loss, lack rice cell-resolved data almost entirely. We extend the discussion beyond transcriptomics to single-cell DNA methylome profiling, spatial proteomics and metabolomics, and three-dimensional analysis of thick plant tissues, in each case distinguishing demonstrated plant capability from mammalian-only capability, and we assess the expanding role of artificial intelligence in annotation, segmentation, batch correction, integration, and perturbation prediction alongside its documented failure modes. Rice, maize, and wheat are compared to identify transferable methodology. Cell-resolved omics has to date improved biological interpretation and candidate prioritization; demonstrating an incremental breeding advantage from it remains an unmet requirement. Full article

Chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis, cystic fibrosis, and chronic bronchitis, are characterized by persistent airway inflammation and mucus hypersecretion, leading to airway remodeling and progressive pulmonary dysfunction. Although current therapies improve disease control, they often fail to adequately target the complex molecular mechanisms underlying chronic airway diseases and may cause adverse effects during long-term use. Natural products have therefore emerged as promising multitarget therapeutic agents because they simultaneously regulate oxidative stress, inflammatory signaling, epithelial dysfunction, and mucus production. Recent evidence demonstrates that marine-derived bioactive compounds and plant-derived phytochemicals modulate key signaling pathways, including nuclear factor-kappa B (NF-κB), mitogen-activated protein kinases (MAPKs), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), Janus kinase/signal transducer and activator of transcription (JAK/STAT), the NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, and nuclear factor erythroid 2-related factor 2 (Nrf2), thereby suppressing airway inflammation, oxidative stress, goblet cell differentiation, and MUC5AC overexpression. Advances in nanoformulation, pulmonary drug delivery, multi-omics, artificial intelligence-assisted drug discovery, and network pharmacology are expected to accelerate clinical translation. Collectively, natural products represent promising candidates for the development of evidence-based functional foods, nutraceuticals, and novel therapeutic strategies for chronic respiratory diseases. Full article

The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and analyze real-time environmental data using specialized IoT sensors capable of monitoring critical variables, including carbon dioxide concentration (CO₂), pH, air temperature, and relative humidity in hydroponic production systems. The proposed framework integrates advanced machine-learning algorithms, including Random Forest Regressor and Long Short-Term Memory (LSTM) neural networks, to process large volumes of environmental data and support crop management. In addition, the platform incorporates Vapor Pressure Deficit (VPD) and Growing Degree Days (GDD) analyses to provide crop-specific recommendations and support informed decision-making. This platform establishes a benchmark for smart agriculture in Mexico’s Laja–Bajío region, facilitating informed decision-making and maximizing the sustainability of food systems. Experimental validation was conducted under both controlled and semi-controlled environments using Swiss chard (Beta vulgaris subsp. cicla L.) and lettuce (Lactuca sativa L.) cultivated in hydroponic systems. These environments represented contrasting climatic conditions, allowing evaluation of platform stability and forecasting performance under varying thermal regimes. The Random Forest Regressor model, trained using growth chamber data consisting of 19,836 valid observations, reproduced the deterministic VPD relationship with a coefficient of determination (R²) of 0.90 and a root mean square error (RMSE) of 0.08 kPa, confirming internal consistency and identifying temperature as the dominant contributing variable rather than predicting an independent outcome. The dynamic alarm system, integrated with crop phenological stages, demonstrated greater effectiveness than conventional static-threshold approaches by generating alerts according to crop developmental requirements. Furthermore, the web-based visualization platform enabled users to interpret environmental conditions through intuitive graphical representations, facilitating decision-making without requiring specialized technical expertise. The results demonstrate the feasibility of the IoP platform as a comprehensive environmental management tool for protected agricultural systems. The proposed framework provides a scalable solution for precision agriculture applications in the Laja–Bajío region of Mexico and in other regions with similar production systems. Full article

Manual visual diagnosis of apple diseases suffers from low efficiency, strong subjectivity and poor scalability for large commercial orchards. Existing research mainly targets diseases on single plant organs, whereas full-growth-cycle detection has to cope with extreme multi-scale differences among lesions. For instance, Valsa canker on tree trunks leads to extensive cortical necrosis, while early-stage anthracnose on fruits appears as tiny spots spanning only a few pixels. These significant scale gaps necessitate robust spatial feature aggregation and anti-noise ability to resist complex background interference. Aiming to achieve rapid and precise detection of diseases on multiple apple organs including leaves, fruits, trunks and branches, this work presents an enhanced YOLOv11 model equipped with the Information Integration Attention (IIA) module. The IIA module is embedded into the key fusion layers of the backbone and neck networks. It strengthens the extraction of fine-grained lesion features, recovers spatial location information via a bidirectional attention mechanism, and suppresses noise induced by uneven lighting and intricate backgrounds. To guarantee stable convergence on low-resource computing devices, a tailored training scheme is designed. Experimental results on a seven-category dataset with 7406 images demonstrate that YOLOv11-IIA reaches a precision of 0.763, a recall of 0.819, mAP@50 of 0.857 and mAP@50-95 of 0.699, which achieves clear performance improvements over the original YOLOv11 (mAP@50 improved from 0.485 to 0.857) and other attention-augmented detectors. The model operates stably on an NVIDIA GTX 1050 4GB GPU with an inference speed of 16 FPS for 640 × 640 input images; comprehensive quantitative computational metrics including parameter count, FLOPs and memory consumption will be fully measured in subsequent UAV deployment experiments. The proposed method provides a reliable technical reference for intelligent apple disease monitoring in smart orchard systems. Full article

The mutualistic association between plants and their seed-associated microbiota has emerged as a key determinant of crop productivity, influencing plant nutrition, immunity, and tolerance to abiotic stress. Seed biopriming, the controlled application of beneficial microorganisms to seeds before sowing, exploits this interaction to enhance germination, seedling establishment, and stress resilience. Unlike conventional chemical priming, seed biopriming induces coordinated molecular reprogramming through changes in the seed metabolome, proteome, and epigenome. This review synthesizes current evidence demonstrating that seed biopriming promotes the accumulation of osmoprotectants, strengthens antioxidant defenses, enhances secondary metabolism, and generates priming-specific proteomic responses. We further examine how these changes interact with phytohormonal signaling networks and epigenetic mechanisms, including DNA methylation, histone modification, and small RNA-mediated regulation, to establish stress memory and improve plant adaptation. The review also discusses recent advances in synthetic microbial communities and nanobiotechnology for improving inoculant stability and efficacy. Despite promising progress, large-scale application remains constrained by inconsistent field performance, formulation stability, and regulatory challenges. Finally, we highlight the integration of multi-omics and artificial intelligence as promising approaches to improve mechanistic understanding, optimize microbial selection, and accelerate the development of reliable seed biopriming strategies for sustainable agriculture under climate change. Full article

Vertical Roller Mills (VRMs) are extensively used in the cement industry for their high energy efficiency. Nonetheless, the start-up phase remains a critical operational challenge due to its pronounced sensitivity to changing process conditions. Such variations frequently induce excessive vibrations, which can trigger unplanned shutdowns, mechanical damage, and diminished throughput. The underlying cause lies in the intrinsic variability of raw materials, particularly in parameters such as moisture, particle size distribution, and hardness, which exert a direct influence on the mill’s dynamic response during the transition to steady-state operation. This study presents the real-world implementation of an Expert System designed to automate the start-up sequence. By applying logical reasoning to key process setpoints, the system enables a controlled and gradual ramp-up, minimizing transient instabilities. Seamlessly integrated into the plant’s control infrastructure, it facilitates remote unattended operation, enhancing process reliability and operational efficiency. The proposed architecture addresses a key challenge in cement production and enables advanced control and intelligent optimization. Full article

As naturally occurring bioactive molecules derived from plants, polyphenols exhibit significant potential for the functional modification and structural regulation of metal–organic frameworks (MOFs) due to their unique ortho-phenolic hydroxyl groups, excellent metal-coordinating ability, and favorable biocompatibility. This review systematically summarizes the functional roles of polyphenols in MOF systems, including their use as organic ligands to directly participate in framework construction, as surface modifiers to optimize MOF interfacial properties, or as encapsulation hosts to enable controlled loading and release. Polyphenol–MOF composites constructed based on these strategies demonstrate broad application prospects in fields such as biomedicine, food science, environmental remediation, and catalysis. This paper further analyzes the key challenges currently facing the research community, including unclear mechanisms of interfacial interactions, insufficient stability assessments under complex conditions, and a lack of scalable green synthesis processes. Future research should delve deeper into the relationship between polyphenol structures and MOF topological configurations and drive the transition from functional composites to functional synergies. These efforts will be key to realizing the practical application of such materials in intelligent food manufacturing, precision medicine, and sustainable environmental management. Full article

Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation precision enabled by iterative operator feedback loops. Full article

Accurate prediction of chiller energy consumption is crucial for the efficient operation and intelligent management of heating, ventilation, and air conditioning (HVAC) systems in large buildings. However, such prediction remains challenging due to the multi-scale temporal coupling and nonstationary dynamics of chiller systems. Therefore, an IMVMD-MADNet hybrid framework integrating Improved Multivariate Variational Mode Decomposition (IMVMD) and a Multi-scale Aggregation Decomposition Network (MADNet) is proposed for chiller energy consumption prediction. To avoid information leakage and capture multi-scale features, a rolling local decomposition strategy with adaptive mode selection is employed. First, IMVMD performs stepwise decomposition within a sliding window, and the optimal number of modes is determined using envelope entropy. Then, sample entropy is used to reconstruct the multivariate modes into high-, medium-, and low-frequency components. Subsequently, a dual-branch MADNet combining wavelet-domain time-frequency modeling (WDP) and time-domain causal dependency modeling (TDP) predicts each component, and the results are aggregated to generate the final prediction. Bayesian optimization is employed to optimize the key hyperparameters. One year of real industrial chiller data from a plant in Huizhou, China, is used to evaluate the proposed model against 11 forecasting models. Results show that the dual-branch architecture outperforms single-branch models. The proposed model achieves the best performance across all forecasting horizons, with its advantage becoming more pronounced as the forecasting horizon increases, demonstrating stable predictive performance under the same-plant setting. Full article