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A shortage of calcium and the resulting physiological problems, like tip burn, create serious difficulties for indoor hydroponic lettuce growing, reducing both harvest and grade. This paper puts forward a unique strategy that merges cutting-edge image segmentation and classification methods to spot calcium insufficiency in lettuce during its growth phases under colored LED light. Recognizing nutrient shortfalls at an early stage is key for prompt correction and efficient nutrient control. The trial cultivated butterhead lettuce in a Deep-Water Culture (DWC) arrangement with managed calcium doses. Image preparation steps, involving Contrast Limited Adaptive Histogram Equalization (CLAHE) and Red Color Correction (RCC), were used to boost picture quality and uniformity, and augmentation was performed to increase model resilience. Our method adopted a two-step workflow that enabled us to exploit the advantages of dedicated models. First, lettuce foliage was separated from the backdrop through state-of-the-art architectures, including U-Net, U-Net++, Recurrent U-Net, and Inception U-Net. U-Net++ showed the top segmentation performance (98.56%) with strong generalization compared to its counterparts. Segmentation extracts the target zone and discards background clutter, permitting the classifier to focus more productively on disease-indicating traits. Deep learning classification networks, namely ResNet and EfficientNet, were used in the second step to identify calcium insufficiency from the segmented pictures. EfficientNetB2 stood out as the most dependable classifier, securing an accuracy of 91.51% on the RCC dataset, while Resnet50 attained a closely matching accuracy of 91.18% on that same dataset. This work emphasizes the promise of weaving deep learning models into automated hydroponic setups for live nutrient tracking, supplying a workable answer to improve output and sustainability in indoor hydroponic operations.

This investigation puts forward a novel intelligent farming platform that merges Internet of Things (IoT) capabilities with predictive modeling and self-regulating command systems to streamline watering processes and amplify resource productivity. The suggested design employs soil wetness, thermal, and atmospheric moisture probes interfaced with an Arduino-centered microcontroller unit, allowing irrigation to be triggered autonomously according to live sensor readings. Agricultural operators can remotely oversee and adjust watering cycles through portable devices, delivering both exactness and operational simplicity. The predictive model fuses historical data with current measurements to anticipate future irrigation demands, guaranteeing moisture delivery exclusively when truly required. Empirical on-farm assessments indicated a 30 % decline in water expenditure versus standard irrigation techniques, whilst upholding ideal earth moisture parameters. In addition, the framework markedly diminished workforce and power expenses, bolstering overall functional capability. By confronting the issue of hydrological shortage and advancing conscientious resource stewardship, this intelligent platform possesses the capability to enhance harvest volumes, curtail greenhouse gas discharges, and further eco-conscious cultivation methodologies. Within water-limited geographies, deploying this technological solution constitutes a pivotal stride towards enduring agricultural viability, indicating considerable scope for extensive implementation.

Health surveillance depends fundamentally on the reliable tracking of individual calves. Yet, prevailing multi-object tracking frameworks are prone to recurrent identification anomalies, particularly ID switches arising under occluded conditions. To overcome this limitation, we introduce YSD-BPTrack in this work, a novel multi-object tracking framework tailored for calves within occlusion-prone farm environments. The architecture is divided into two principal stages: detection and tracking. For the detection stage, DCNv4 is embedded into the YOLOv8s model to learn spatial deformations caused by occlusion, thereby strengthening detection robustness under such conditions. Meanwhile, the Star operation borrowed from StarNet is adopted to deliver high detection capability while keeping computational demands low. In the tracking stage, we first devise an original rematching mechanism (Rematching module) alongside a redesigned trajectory removal policy (Trajectory removal module). The Rematching module exploits expanded trajectory prediction boxes to re-establish correspondences with detection boxes during occlusion events, thereby cutting down on ID switch errors. The Trajectory Removal module, in turn, flexibly governs the timeout period for lost trajectories, minimizing the risk of premature trajectory deletion. Concretely, the framework we propose sets a new HOTA (Higher Order Tracking Accuracy) score of 91.6%, leading competing approaches in both tracking quality and runtime performance. Experimental evidence further demonstrates the advantage of YSD-BPTrack, with HOTA climbing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.4%, accompanied by reductions of 49.1% in parameters, 88.9% in IDSw (Identification Switches), and 39.2% in computational overhead relative to the other frameworks. Collectively, the presented multi-object tracking framework carries strong potential to redefine calf tracking practice.

Grass carp hemorrhagic disease (GCHD), which is induced by grass carp reovirus (GCRV), stands as one of the most severe communicable diseases jeopardizing freshwater aquaculture, creating a demand for innovative, effective, and affordable surveillance tools. Machine vision can serve as a powerful inspection method, enabling early alerts for GCHD epidemic spread. Yet, experimental dataset tests indicate that the detection precision of existing deep learning algorithms remains unsatisfactory. In the present study, image datasets of the model organism Chinese rare minnow (Gobiocypris rarus) manifesting GCHD signs were gathered, and a self-attention-based convolutional neural network (SA-CNN) deep learning architecture was designed. This SA-CNN framework is capable of reliably pinpointing Chinese rare minnow individuals displaying GCHD symptoms. Through pre-processing the extracted imagery using refined k-means and improved watershed algorithms, tightly clustered or superimposed fish can be adaptively disentangled within populations, thereby elevating recognition accuracy. With an optimized loss function, the SA-CNN model delivers an accuracy of 99.1%, a recall of 97.7%, and an F1 score of 96.6% on the collected datasets of symptomatic fish. Its precision outperforms current state-of-the-art you only look once (YOLO) network families by margins between 4.9% and 25.6%, varying across different model variants. Image identification demands a mere 95ms per frame, highlighting considerable operational efficiency. Ablation studies confirmed that incorporating the self-attention module yielded accuracy gains of 3.8% and recall improvements of 3.0%. Accordingly, this refined SA-CNN framework furnishes an efficacious solution for the essential large-scale deployment of GCHD surveillance initiatives tracking GCRV transmission, paving the way toward intelligent, health-oriented, and precision aquaculture.

Guaranteeing end-to-end visibility, certified conformity, and uninterrupted temperature control in frozen food logistics remains an enduring obstacle, compounded by siloed oversight tools, hands-on inspection routines, and exposure to information falsification. This paper puts forward a blueprint for a blockchain-anchored conformity framework that resolves these difficulties by fusing continuous Internet of Things (IoT) sensing, authorized blockchain-based recordkeeping, and automated conformance enforcement via smart contracts. Adopting a Design Science Research (DSR) approach, the inquiry zeroes in on the formative stages (challenge scoping, goal setting, and artefact blueprinting) to deliver a methodical grounding for subsequent testing and appraisal. The envisioned construct is arranged across three mutually reliant strata: (1) a data acquisition layer that secures uninterrupted and safeguarded sensor-fed tracking; (2) a data storage layer that harnesses blockchain for indelible logs and open verifiability; and (3) an application layer that weaves in smart contracts for hands-off compliance activation along with dashboards for actor engagement. By reinterpreting statutory conformity mandates into a compartmentalized, blockchain-centric blueprint, this investigation fortifies the conceptual basis of decentralized governance apparatuses within agri-food networks. The formulated framework codifies design maxims capable of shaping parallel provenance systems in other heavily audited supply networks. While practical corroboration is yet to come, the architectural outline supplies a platform for later DSR rounds and enriches design intelligence in the arena of digital conformity infrastructures.

Reliable, hands-off measurement of bovine body dimensions and weight is vital to increasing output and accelerating genetic gains in tropical beef enterprises. Traditional techniques are time-consuming, demand heavy machinery, require handling livestock, induce animal stress, and are frequently unworkable for resource-constrained smallholders lacking adequate infrastructure. This paper introduces an original, smartphone-operated platform for instantaneous body dimension capture and weight forecasting in Brahman cattle, driven by a cloud-based artificial intelligence (AI) engine. A corpus of 12,660 lateral photographs was assembled and tagged for hip depth (HD) and body length (BL), with YOLOv11 convolutional neural network variants undergoing training and evaluation. The YOLOv11m configuration achieved the highest metrics (precision: 99.85%, recall: 100%, F1-score: 99.92%, IoU: 97.68% ± 1.31%), and the automatic extractions displayed tight concordance with manual ImageJ-derived figures (MAPE < 0.4%). HD and BL were strongly intercorrelated in both sexes (r = 0.98–0.99) and moderately indicative of body weight (r = 0.57–0.59). A multiple regression formula incorporating HD and BL delivered the most precise body weight predictions (MAE = 43.44 kg; MAPE = 8.91%). The system was fielded via a LINE messaging chatbot interface, allowing users to upload cattle photos and receive immediate dimensional outputs and weight approximations straight to their smartphone—doing away with any need for bespoke hardware. This inexpensive, easy-to-operate AI instrument supplies a scalable avenue for digital phenotyping, herd oversight, and evidence-based selection in smallholder environments. The concept exhibits substantial capacity to enable data-informed management and enduring productivity improvements in tropical beef networks.

Quantifying soil total nitrogen (TN) with high accuracy is vital for boosting crop vigor and quality. A major obstacle, however, is that soil diversity from one region to another undermines the transferability of calibration models. To confront this obstacle, this paper puts forward a framework called transfer-learning-assisted laser-induced breakdown spectroscopy for cross-regional soil analysis (TransLIBS-CRS), which is custom-built to counteract the weak cross-domain predictive capacity caused by differences in regional soil characteristics. By adapting the model with a small batch of samples from the target setting, the technique substantially elevates the cross-domain utility of LIBS measurements, easing the difficulties tied to procuring soil specimens from geographically dispersed locations. In tests where Beijing data were used to infer TN levels in Guangzhou, the TransLIBS-CRS model yielded the strongest outcomes, recording an R2V of 0.846 and an RMSEV of 0.814 g/kg. A closer look via saliency mapping and chemometric tools indicated that carbon emission wavelengths at 193.0 nm and 247.8 nm serve a decisive function in TN quantification. Crucially, these spectral markers kept their predictive power unchanged when carried over to the Guangzhou soil dataset. This tactic furnishes a workable route toward broad-scale, high-efficiency soil TN assessment.

The present investigation tackles several pressing difficulties in modern poultry operations, namely pathogen transmission, workforce deficits, inadequate surveillance capacity, and intensifying requirements for superior animal welfare standards. To meet these demands, an elaborate machine vision framework based on deep learning is put forward, wherein the YOLOv11 algorithm is deployed to autonomously observe and administer poultry. A collection of 330 chicken photographs was gathered from Thai agricultural facilities under a range of environmental circumstances and subsequently increased to 1,716 images by means of rotation, along with alterations to brightness and saturation parameters. Training of the YOLOv11 architecture was carried out on the NVIDIA Jetson Orin Nano Developer Kit, with performance gauged through precision, recall, and mean average precision (mAP) metrics. The architecture attained remarkably high fidelity, registering precision of 0.964, recall of 0.938, and mAP of 0.963, correspondingly. The fusion of optical with thermal imaging permitted the apparatus to circumvent limitations imposed by variable illumination and ambient settings. Outcomes from practical deployment evidenced real-time observation and tracing of numerous birds, thereby supporting disease mitigation, feed optimization, and holistic farm governance. This inquiry advances the domain of intelligent agriculture by furnishing a wholly automated, expandable, and exceedingly accurate poultry oversight infrastructure designed to foster ecological responsibility, operational productivity, and elevated animal welfare within contemporary poultry cultivation.

Interest in sustainable farming approaches has surged, especially within hyper-arid environments where acute water shortages make food security a pressing priority. This increased focus arises from the goal of agricultural independence in light of geopolitical volatility, rising population numbers, climate disruption, and the drive to decrease reliance on imported food. The present work investigates novel, data-centric methods specifically engineered for agriculture under such demanding circumstances, concentrating on the exploitation of CO₂ enrichment to amplify resource efficiency by accelerating plant development and fine-tuning input application within closed greenhouse structures. Within this study, a data-driven model predictive control (MPC) system is deployed in a greenhouse context to refine irrigation timing. The primary emphasis lies in harnessing the capability of an extreme gradient boosting (XGBoost) model to project real-time transpiration fluxes, accounting for the complex interdependencies between microclimatic variables and physiological shifts that influence transpiration. The XGBoost framework is designed to ingest microclimate observations—namely solar radiation, internal air temperature, internal relative humidity, and internal CO₂ molar fraction—as well as vegetation indices extracted from hyperspectral reflectance measurements, including NDVI, WBI, and PRI, all serving as forecasting inputs. The predictive model achieved strong fidelity, registering an R² of 97.1% alongside an RMSE of 0.417 mmol/m²/s for transpiration rate estimation. The trained XGBoost is subsequently embedded into the MPC scheme to steer irrigation delivery while upholding target soil water status. This integrated controller is then applied to govern watering tactics across two differing CO₂ supplementation settings: 400 ppm and 1000 ppm. Outcomes from this work reveal that the MPC-driven irrigation regime yields a 42.2% reduction in water use across a one-week forecast horizon when contrasted with the baseline irrigation protocol, irrespective of CO₂ level. Moreover, operating the MPC controller under the distinct CO₂ regimes shows a 34% decrease in water consumption at the 1000 ppm enrichment level relative to the 400 ppm condition. These findings highlight the value of MPC within enclosed greenhouse facilities, stressing the gains offered by advanced predictive analytics, multi-source data fusion, and continuous receding-horizon optimization for realizing superior irrigation regulation. The work equally accentuates the role of CO₂ fertilization in sealed agricultural greenhouses—particularly in zones subject to intense solar irradiance—as a potent measure for curbing water expenditure.

Crop production around the globe is increasingly at risk from invasive organisms, particularly the Fall Armyworm (FAW, Spodoptera frugiperda), which has caused severe deterioration of maize harvests across both Africa and Asia starting in 2016. To enable targeted pest control, an adaptive Echo State Network (ESN) was constructed that projects yearly maize output while factoring in FAW burden. A 15-year (2010–2024) monthly repository was built, integrating vegetation indicators from satellites, field-level meteorological measurements, soil nutrient readings, and FAW observation tallies. The intensity of FAW infestation is represented on a continuum from 0 to 100, derived by fusing monthly trap catch figures (40%) with larval concentration (60%). The ESN is initially calibrated on the entire body of environmental features to estimate crop performance. Next, isotonic regression is deployed to link infestation intensity with the ESN's positive residual errors, yielding a monotonic penalty mapping. This mapping captures the magnitude of output depression at varying infestation levels. When generating estimates, the inferred penalty is applied to the unadjusted ESN projection, modifying harvest forecasts to incorporate pest-driven losses without changing the foundational ESN configuration. In testing across held-out folds, the FAW-sensitive ESN yields an R² near 0.55 and cuts forecast discrepancies by as much as 67% compared to unadjusted reference models, faithfully reproducing documented harvest declines beyond 20% during major infestation episodes. The approach beats standard regression and deep neural architectures by comparable margins. It supports agriculturalists in directing countermeasures toward high-threat areas, lowering both chemical input volumes and operational expenditures. These outcomes emphasize its promise as a forward-looking alert mechanism for pinpointed actions that curtail agrochemical usage and improve input distribution. Ongoing field assessments will gauge its capacity to scale and its tangible efficacy within maize-growing zones affected by FAW.

As urban landscaping advances, the struggle with garden pests and diseases grows more critical. Substantial interest has been drawn to large language models for their skill in interpreting what users mean and producing suitable responses. The addition of knowledge graphs has offered these models a well-organized, high-quality foundation of facts. This investigation merges knowledge graphs (KGs), large language models (LLMs), and similar tools to engineer a smart question-answering (Q&A) framework for garden pest and disease contexts. Forge a knowledge graph dedicated to garden pests and diseases by amassing dependable data through web scraping and scholarly review. Pinpoint central entities and their links to shape a conceptual schema. Employ the ERNIE-BiLSTM-CRF architecture to pull facts from unstructured text. Testing reveals the fact-extraction system advanced in this work scores above 92% on precision, recall, and F1 metrics, beating competing designs. Put forward a Q&A strategy that fuses the garden pest and disease KG with the ERNIE-Bot-turbo engine. Knowledge is turned into vectors, and the closest matches are located via similarity search; these top hits are then bundled with the query into cues fed to the language engine for natural language output. Trials stacking our technique against ERNIE-Bot-turbo and ChatGLM-6B showed strong results across easy, mid-level, and tough problems, sidestepping off-base replies to out-of-scope questions. It exceeded both baselines in correctness, hitting a 90% exact-answer mark for straightforward queries.

Precise categorization of avocado ripeness plays a vital role in strengthening post-harvest operations and curbing waste throughout agricultural supply networks. The present study centers on constructing a resilient ensemble classification framework employing spectral readings from 120 kilograms of Buccaneer avocados procured from the Royal Project in Chiang Mai, Thailand. The specimens were assessed via near-infrared (NIR) spectroscopy at 18 wavelengths. Five distinct machine learning models—Random Forest, Decision Tree, XGBoost, Gradient Boosting, and Gaussian Mixture Model—were separately trained and subsequently fused into a unified ensemble. Four distinct algorithms were deployed to fine-tune the weighting of each model: Bayesian optimisation, differential evolution, particle swarm optimisation, and grid search. Evaluation metrics covering accuracy, precision, recall, F1-score, confusion matrices, and ROC curves were applied. Grid Search yielded the strongest classification outcomes, securing an accuracy of 82.5% alongside an F1-score of 85.3%, emphasizing the value of weight-refined ensemble learning relative to standalone classifiers. This investigation delivers a scalable and transparent pipeline for non-destructive ripeness identification. The outcomes, notwithstanding constraints related to overfitting and dependency on spectral data integrity, pave the way for eventual real-time field deployment in agriculture.

Precise tracking and forecasting of dissolved oxygen (DO) concentrations in aquaculture environments are essential for sustaining ideal water quality and safeguarding fish well-being. This research introduces an improved Long Short-Term Memory (LSTM)-based framework for DO forecasting that leverages historical records of DO concentrations, water temperature, and other environmental variables. In contrast to conventional techniques that rely on static assumptions, the proposed framework dynamically adapts to changing environmental conditions, delivering immediate, high-accuracy predictions. Experimental results show that the improved LSTM framework achieves a forecasting precision of 92.28%, surpassing current models, including IFP (81.97%), DOE (63.81%), and DOP (86.79%). The framework's exceptional precision and flexibility make it a reliable tool for aquaculture oversight, helping optimize DO levels and reduce the risk of fish deaths. By incorporating artificial intelligence into aquaculture surveillance, this method enhances system performance and long-term viability.

The capacity to detect rapeseed with precision and to track the enlargement of its canopy coverage stands among the most telling phenotypic signals of how the crop is developing. When both the individual rapeseed plant and the zone it occupies can be accurately recognized, the resulting data stream provides a powerful foundation for downstream phenotypic exploration and breeding programs. The difficulty, however, is that in genuine field conditions, detection performance continues to face serious obstacles because RGB data alone provides only a limited vocabulary of representable features. Seeking a way around this limitation, the work described here advances a dual-modal instance segmentation framework, MSNet, which builds on YOLOv11n-seg and combines RGB and Near-Infrared (NIR) data streams. The chief innovations embedded in this framework comprise three strategies governing where fusion occurs (at the front end, at an intermediate stage, or at the back end), together with a newly crafted Hierarchical Attention Fusion Block (HAFB) dedicated to blending multi-modal features. Experiments designed to compare these placement choices reveal that conducting fusion at the intermediate stage delivers the strongest compromise between how accurately the model detects and how economical it remains in terms of parameter count. Relative to the baseline, the improvement in mAP50:95 can reach 3.5%. Once the HAFB unit is incorporated, the MSNet-H-HAFB configuration posts a 6.5% lift in mAP50:95 over that baseline, while requiring fewer than 38% extra parameters. A point that deserves emphasis is that mid-stage fusion consistently outperformed the alternatives in every experimental run, supplying a clear blueprint for where future multi-modal architectures should position their fusion operations. In further testing, side-by-side comparisons against a suite of RGB-only instance segmentation alternatives confirmed that all MSNet-HAFB fused variants decisively beat single-modal counterparts on rapeseed count detection, reinforcing the case that multi-spectral fusion holds genuine promise for recognizing agricultural targets. As a concluding step, MSNet was applied in a practical agricultural scenario that included vegetation index analysis and grading of frost-related damage. The data pointed to ZN6–2836 and ZS11 as likely candidate elite lines, while the EVI2 vegetation index registered the strongest classification scores for frost damage in rapeseed.

A precision spray perception system using LiDAR was designed, algorithmically developed, and experimentally verified to address the problem of low navigation line extraction accuracy in self-propelled sprayers, which causes wheel encroachment on ridges and pesticide overuse. For this perception system, a data processing framework was built encompassing preprocessing, adaptive segmentation separating crops from ditches, and the derivation of navigation lines and crop positioning, all originating from raw LiDAR point cloud species. To validate the system’s stability, field data on cabbages at different growth stages were collected and analyzed. A controllable, constant-speed experimental configuration was set up to benchmark LiDAR against a depth camera within the same field conditions. The trial outcomes reveal that at sprayer speeds of 0.5 and 1 ms⁻¹, the peak lateral deviation reaches 0.112 m within cabbage ridge settings containing inter-row weeds. In comparison, the mean absolute lateral error stands at 0.059 m. Each frame is processed in no more than 43 ms. Compared with the machine vision approach, the proposed technique reduces the mean processing time by 122 ms. In terms of identification accuracy, computational speed, and overall robustness for crop detection and navigation line derivation, the system introduced here outperforms its machine vision counterpart.

Edge devices are increasingly regarded as a portable, budget-friendly alternative to conventional personal computer terminals, offering a promising route toward downsizing equipment and highly flexible robotic fruit harvesting at the field scale. The present study proposes a compact, optimized You Only Look Once version 8n (YOLOv8n) architecture tailored for Orah fruit detection and subsequent deployment on an edge computing platform. Initially, ADown modules were brought into the design to shrink the overall model footprint while safeguarding detection performance. Next, a Concentrated-Comprehensive Dual Convolution (C3_DualConv) component, built around paired convolutional bottlenecks, was devised to reinforce the network’s ability to perceive Orah fruits partly hidden behind branches and foliage; this intervention also contributed to additional model compression. Furthermore, a Bidirectional Feature Pyramid Network (BiFPN) equipped with a high-resolution layer at pyramid level 2 was adopted to facilitate more powerful multi-scale feature aggregation. In parallel, three Coordinate Attention (CA) mechanism modules were embedded to sharpen the system’s sensitivity to Orah fruit features. Finally, a more targeted minimum points distance intersection-over-union loss formulation was applied to improve detection throughput for Orah fruits experiencing dense mutual occlusion. Experimental trials confirmed that the enhanced YOLOv8n architecture reliably pinpointed Orah fruits operating inside challenging orchard scenes, delivering precision of 97.7%, Average Precision at IoU threshold 0.5 ([email protected]) of 98.8%, and an F1 score of 96.69%, all while occupying a lean 4.1 MB of storage on a Windows-based system terminal. The resulting model was then optimally transferred to an Nvidia Jetson Orin Nano via the TensorRT Python Application Programming Interface (API), achieving a mean interface throughput of over 30 fps and thereby verifying real-time detection performance. The outcomes of this work stand to offer technical foundations for Orah fruit robotic harvesting powered by edge-based computing.

Within the densely structured environment of an orchard, pinpointing the exact harvesting location is fundamental to the autonomous operation of fruit-picking robotic systems. Despite this, current techniques are prone to miscalculations in positioning under intricate conditions such as abbreviated stalks, partial concealment, or instances where the target is entirely misread, any of which can compromise the robot's operational efficiency in the field. To address complex vineyard harvesting scenarios, this paper presents an advanced method for picking-point determination that relies on semantic-level inference. The approach introduces three novel components: the semantic reasoning module (SRM), the ROI threshold adjustment strategy (RTAS), and the picking point location optimization module (PPOM). The SRM is deployed to resolve cases where obstructions mask grape stalks, suffer from partial recognition errors, or undergo total recognition failure. The RTAS is designed to overcome difficulties associated with stalks that are positioned low and are notably short during the picking sequence. In the final stage, the PPOM adjusts the calculated picking point coordinates to their optimum, granting the manipulator greater maneuverability as it executes the harvest action. Empirical validation demonstrates that SegFormer produces an mIoU (mean Intersection over Union) of 84.54%, accompanied by B_IoU and P_IoU scores of 73.90% and 75.63%, respectively. In addition, the refined fruit-picking point localization method achieved a success rate of 94.96%, representing an improvement of 8.12% over the baseline technique. The average computation time stands at 0.5428 ± 0.0063 s per instance, which is adequate for real-time harvesting demands.

Cotton growth is severely threatened by Verticillium wilt, which has a considerable negative effect on yield. For this reason, prompt identification of stress-related disease is critically important. The current work explored how Verticillium wilt stress influences leaf microstructure and physiological metrics (SOD, POD, CAT, MDA, Chla, Chlb, Chlab, Car) in cotton and tested whether hyperspectral imaging could be used to estimate those leaf physiological metrics. Results revealed that disease-induced stress altered the cellular architecture of cotton leaves, leading to the breakdown and disintegration of chloroplasts and mitochondria. Moreover, infected leaves showed significantly higher SOD and POD activities, as well as elevated MDA levels, relative to their healthy counterparts, while chlorophyll and carotenoid concentrations dropped markedly. Furthermore, rapid-assessment models for cotton physiological metrics were built, with the strongest models attaining Rp values spanning 0.809 to 0.975. These models were then employed to generate visual maps of the distribution of physiological signatures over cotton leaf surfaces. The outcomes indicate that the physiological phenotype of cotton leaves can be reliably captured using hyperspectral imaging, providing a solid theoretical foundation for rapid identification of Verticillium wilt stress.

The root system is essential not only for nutrient uptake and anchoring the plant but also for shaping crop development and responses to environmental stress. Rhizobox setups permit in situ, non-invasive root observation in soil, representing an economical imaging strategy. Nevertheless, soil’s non-transparent nature frequently produces discontinuous root images with intermittent gaps, diminishing the precision of derived root phenotypic metrics. We propose a root inpainting methodology grounded in a Generative Adversarial Network (GAN) framework. Furthermore, a hybrid root inpainting dataset (HRID) was assembled, containing 1206 cotton root images with genuine gaps and 7716 rice root images with synthetically introduced gaps. Compared with computationally simulated root depictions, our dataset provides authentic root system architecture (RSA) and textural characteristics. Rather than cropping during training, our technique uses downsampled versions of the full image to convey the complete root morphology. Training the model uses binary cross-entropy loss to differentiate between root and background pixels.Additionally, Dice loss is incorporated to counteract the effects of uneven class representation. Skip connections typical of U-Net are removed, and an edge attention module (EAM) is introduced to retain more granular structural details. Compared with competing approaches, our technique substantially increases recall from 17.35% to 35.75% on a cotton root test collection of 122 images, indicating marked gains in inpainting fidelity. For root area, root length, convex hull area, and root depth, the trait error reduction rates (TERRs) reach 76.07%, 68.63%, 48.64%, and 88.28%, respectively, delivering significantly more accurate root phenotyping outcomes. Both the EU-GAN source code and the 8922 annotated images are openly accessible, offering potential reuse in other AI-driven investigations. This work provides a resilient solution for root phenotyping, thereby accelerating breeding workflows and expanding knowledge of root system behavior.

Agricultural output and the assurance of food supplies depend fundamentally on plant breeding. A new chapter in this field has been opened by Genomic Selection, a technology distinguished by its power to exploit variation across the entire genome for predictive purposes. This technique makes it unnecessary to have pre-existing knowledge of the genes controlling particular characteristics. Yet, the enormous scale of genomic information, contrasted with the usually scarce set of phenotypic observations, frequently leads to the “curse of dimensionality,” in which classic statistical approaches, machine learning, and deep learning architectures tend to memorize noise rather than learn patterns, yielding poor predictive performance. To address this barrier, we propose a consolidated Variational auto-encoder-based Multi-task Genomic Prediction model (VMGP) that fuses a self-supervised process of shrinking and rebuilding genomic data with several simultaneous forecasting objectives. This method provides a durable solution, offering a powerful inferential engine that has undergone rigorous verification across publicly available datasets for wheat, rice, and maize. The model we have built displays extraordinary proficiency in forecasting across multiple phenotypes and varying environments, effectively mastering the challenges of genomic selection spanning different populations and highlighting its distinct capabilities and practical importance. In addition, by fusing VMGP with tools for model transparency, we can efficiently sift through and prioritize influential single-nucleotide polymorphisms, thereby boosting prediction accuracy and putting forward avenues for potentially more economical genotyping workflows. The VMGP framework, characterized by its clean design, strong forecasting capabilities, and publicly accessible source code, is well-suited for widespread rollout across plant breeding operations. It proves especially valuable for practitioners whose main focus is phenotypic projection, yet who might not be deeply versed in deep learning methodologies or the intricacies of hyperparameter optimization.

The push toward precision agriculture is increasingly driven by environmental considerations alongside economic pressures, fueling progress in variable-rate fertilization (VRF) technology. Although centrifugal fertilizer spreaders offer benefits such as mechanical simplicity, low cost, and high throughput, non-homogeneous spread patterns are becoming a primary obstacle to their broader use. An urgent investigation is therefore required into how particles behave and what variability patterns emerge when centrifugal VRF spreaders operate under multi-pass overlapped application, to boost both evenness of coverage and field accuracy. This paper examined and assessed the operational characteristics of a custom-built centrifugal VRF spreader that uses real-time crop status data for rice and wheat, applying the collection tray protocols detailed in ISO 5690 and ASAE S341.2 (1999). Distribution uniformity was evaluated by calculating the coefficient of variation (CV) from weighed fertilizer captured in standardized trays, thereby gauging the evenness of the applied spread profile. The obtained data indicated effective swath widths of 21.05, 22.58, and 23.67 m corresponding to target application dosages of 225, 300, and 375 kg/ha, respectively. In practice, multi-pass, overlapped spreading tended to deliver application amounts exceeding the intended rates, with within-swath CVs of 11.51, 9.25, and 11.28% for each respective dosage level. Field-based assessments of multi-pass, overlapped operations recorded a mean discrepancy of 4.54% between delivered and target rates, along with a mean lateral distribution CV of 11.94% across the implement’s working span, results that align with the performance criteria for transverse particle placement using centrifugal broadcasters. The insights generated by this work provide a conceptual reference point for the design refinement and technological evolution of centrifugal VRF equipment, carrying considerable applied and societal value in hastening their integration into everyday precision farming practice.

To address poor accuracy in identifying open-field vegetables when light is blocked, this investigation used cabbage as the subject. It established an online target identification model based on deep learning. Using YOLOv8n as the backbone network, an approach was formulated to reduce the impact of light occlusion on the precision of online cabbage identification. A sequence of cabbage image filters was engineered to remove the consequences of light occlusion. A unit for adaptive parameter acquisition tailored to the cabbage image filters was built. The image filter sequence and the adaptive acquisition unit were woven into the YOLOv8n object detection architecture. This fusion enabled precise, real-time identification of cabbage in light-occluded environments. Experimental outcomes indicated identification precisions of 97.5% with the normal lighting dataset, 93.1% with the light occlusion dataset, and 95.0% with the mixed dataset. When the degree of light occlusion exceeded 0.4, the identification precision increased by 9.9% and 13.7% compared with the YOLOv5n and YOLOv8n architectures. The model achieved identification precisions of 99.3% with the Chinese cabbage dataset and 98.3% with the broccoli dataset. The model was deployed on an Nvidia Jetson Orin NX edge device, where it processed images at 26.32 frames per second. Field assessments showed identification precisions of 96.0% in ordinary light and 91.2% when light was blocked. The presented online cabbage identification model facilitates instant recognition and spatial location of cabbage in complex open-field conditions, providing technical support for targeted spraying.

Precision livestock farming depends critically on the ability to identify individual animals. One practical route for marking individual livestock is freeze branding with liquid nitrogen. With the march of technological progress, deep learning pipelines have begun to be deployed for reading such individual markings. The present work introduces a deep-learning-based pipeline for the localization and interpretation of oriented horse brands. In the first stage, Rotational YOLOv5 (R-YOLOv5) was harnessed to pinpoint the oriented brand, after which YOLOv5 processed the extracted brand patches to read the digits. Within that first stage, R-YOLOv5 departed from standard detection frameworks by embedding orientation directly into the YOLO architecture through Circle Smooth Label (CSL). In addition, Coordinate Attention (CA) was woven into the network to sharpen its focus on spatial positioning cues. These design choices boosted the fidelity with which oriented brands were detected. The second stage reframed digit reading as an object-detection problem, given the need for precise identification. The full brand number was then assembled by ordering the detection boxes according to their spatial coordinates. Experimental comparisons revealed that R-YOLOv5 outpaced competing rotating target detection methods, achieving an AP (Average Accuracy) of 95.6%, FLOPs of 17.4 G, and an inference rate of 14.3 fps. The digit reading stage delivered an mAP (mean Average Precision) of 95.77%, a weight footprint of 13.71 MB, and an inference rate of 68.6 fps. Through this two-stage design, brand numbers can be read reliably even against cluttered backgrounds, offering a robust yet lightweight solution for individual livestock identification.

Automated phenotyping, the automated measurement of plant characteristics, supports breeders and growers in producing vigorous, resilient crops. An early-detection tool can accelerate the discovery of plant resistance and swiftly identify problematic breeding outcomes. Numerous phenotyping objectives of this kind can be met by analyzing images captured by simple, inexpensive RGB-D cameras. The present work focuses on a case study — assessing the degree of resistance that tomato hybrids exhibit against the tomato yellow leaf curl virus (TYLCV) in commercial greenhouses. This poses a tough challenge, given that discriminating between resistance classes from imagery is difficult even for seasoned breeders. A substantial image collection was assembled from a trial encompassing many tomato hybrids with different resistance ratings. Depth data served to pinpoint the highest section of the tomato plant. Subsequently, deep learning networks were deployed to categorize the distinct resistance classes. For spotting plants with visual indicators, our techniques achieved an accuracy of 0.928, a precision of 0.934, and a recall of 0.95. In the multi-class scenario, an accuracy of 0.76 was achieved in identifying the appropriate level, with an error of 0.278. Our pipelines are not exclusively tailored for this task and can be expanded to other undertakings that detect various plant diseases with outward signs, including ToBRFV, mildew, ToMV, and others.

The quantification of actual evapotranspiration (ETₐ) is fundamental to sound water resource governance, yet current estimation methods pose significant constraints. Established methodologies, among them eddy covariance and energy balance computations reliant on remote sensing, are frequently beset by prohibitive costs, patchy spatial and temporal coverage, and deteriorating forecast fidelity, particularly for older empirical constructs. Although machine learning has emerged as a viable solution, it is not without drawbacks, especially a systematic bias toward low ETₐ predictions during high-temperature episodes. We trace this deficiency to inadequate model training on the scarce but critically significant extreme ETₐ values, paired with meteorological datasets that are limited rather than massive. Within this paper, we illustrate how few-shot, meta-learning frameworks (MAML), purpose-built to deliver superior generalization when data are constrained, can eclipse elementary machine learning formulations in the upscaling of ETₐ originating from two major in-situ observation networks, namely Ameriflux and Euroflux. Working with a sparse set of remotely sensed land surface variables sourced from the METRIC-EEFlux system alongside a narrow suite of climatic drivers, we establish that the selected models yield measurable benefits when assessed under the utility-based regression philosophy, guiding meaningful real-world decisions. Our preliminary findings show that EEflux ETₐ strays markedly from in-situ tower readings from the Ameriflux and EEflux installations (R² = 39%). By comparison, MAML attains the strongest alignment with ETₐ, outperforming both elementary machine learning architectures and EEFlux (R² = 71% on the full test corpus, R² = 0.88 under the Csa climate class, R² = 0.79 under the Cfa climate class, and R² = 0.78 for the CSH vegetation category), and steadily improves without succumbing to overfitting even when trained on a comparatively modest data pool. Its elevated F2 score (96%) indicates that MAML maintains high precision and recall for atypical instances, a feature of considerable consequence for irrigation scheduling. As a noteworthy result in its own right, this investigation corroborates that even a sparse set of remotely sensed EEflux outputs contributes materially to understanding ground-referenced ETₐ and can thus serve a valuable role in environments where the procurement of abundant, high-quality data is limited.

The choice of methodologies is a pivotal factor in disease identification, particularly when computer vision intersects with agricultural innovation. Safeguarding global food supplies depends on the timely and accurate identification of crop diseases. Deep learning offers a feasible pathway to satisfy this demand. To move this research forward, we engineered and tested a disease identification framework grounded in a novel ensemble strategy. Our contribution includes the presentation of DenseNetMini, a more compact iteration of the DenseNet architecture. We advocate merging DenseNetMini with a learning resizer via an ensemble approach to improve training fidelity and accelerate knowledge acquisition. A further innovative element involves deploying Gradient Product (GP) as the optimization mechanism, efficiently reducing the training time while simultaneously improving model performance. Investigating imagery at varying magnifications reveals remarkable improvements in diagnostic consensus and precision. Accuracy figures on test data reach 99.65%, 98.96%, and 98.11% for the Plantvillage, Tomato leaf, and Appleleaf9 collections, respectively. A core triumph of this investigation is the substantial reduction in computational time, suggesting that GP adoption could make agricultural disease screening more accessible and operationally efficient. Beyond numerical gains, the work foregrounds Explainable Artificial Intelligence (XAI) approaches as indispensable for enhancing the interpretability and clarity of the disease detection framework. By visually pinpointing diagnostically relevant zones on foliage, XAI enhances the model’s comprehensibility, thereby nurturing user confidence and insight into how it arrives at its conclusions.

Over the past few years, intense genetic pressure for rapid weight gain and elevated breast meat output has left the international poultry sector wrestling with the difficult condition known as woody breast (WB). This myopathy has inflicted massive economic damage, estimated at up to $200 million each year, and its fundamental cause has still not been pinpointed. Manual tactile assessment remains the standard approach for distinguishing WB fillets from regular ones, but this technique is labour-intensive and prone to human inconsistency. Hyperspectral imaging (HSI) combined with machine learning algorithms can assess fillet WB status in a non-invasive, impartial, and high-throughput manner. For the present work, a total of 250 uncooked chicken breast fillet specimens—spanning normal, mild, and severe categories—were collected, and, for the first time, the spatially unequal distribution of hardness was taken into account in the design of HSI-based processing models. This investigation not only categorized WB grades from HSI data but also formulated a regression framework linking the spectral inputs to measured sample firmness values. To build an effective classification and regression pipeline, a neural architecture search (NAS) strategy was used to construct a wide-deep neural network, christened NAS-WD. In this design, NAS was initially deployed to tune both the network layout and its hyperparameters autonomously. Classification outcomes indicated that NAS-WD achieved 95% total accuracy in classifying the three WB grades, exceeding the performance of classic machine learning methods. At the same time, the regression model linking spectral signatures to hardness achieved an R² of 0.75. This result substantially surpasses that of conventional regression techniques.

The advancement of motion-sensor technology for observing cattle behavior has enabled farmers to assess their animals’ welfare more efficiently. Whereas the bulk of existing research focuses on single-dimensional outputs and separate behavioural classifications, superior predictive precision can be achieved by integrating intricate movements and enhancing sensor algorithms that detect multidimensional activity, that is, more than one movement executed concurrently. This article presents a machine-learning methodology for analyzing overlapping yet independent motions. The system’s output comprises automatically identified complex behaviour sequences that may be used to gauge animal welfare, forecast calving, or detect early disease indicators. The investigation pairs automated motion-sensing equipment (namely, a halter and a pedometer) developed for ruminants and sold under the name RumiWatch with video-based surveillance. It places it upon a Charolais fattening bull. Fourteen distinct categories of complex motion were catalogued: defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. Owing to the deployment of several parallel binary classifiers, the platform proved capable of identifying concurrent behavioural patterns with high fidelity. Two machine learning methods, Support Vector Classification (SVC) and Random Forest, were applied to distinguish various generalised and non-generalised motion types. Outputs produced by these two supervised learning frameworks were placed side by side for comparison. An uninterrupted forty-eight-hour video stream was labelled to serve as training material for the systems and to corroborate their predictive outputs. The performance levels of both classifiers in pinpointing specific motions, whether using both sensors or each sensor individually, and under varying parameter configurations (i.e., window size and padding), were scrutinised. Although the two classifiers yielded divergent results, the optimised parameter configurations indicated that each motion category exhibited by the subject animal was successfully identified with substantial accuracy. Further research encompassing a greater number of individual subjects and different ruminant species would broaden the knowledge base regarding improvements to the system’s efficacy and precision.

Assessing the extent of plant injury is a vital element of crop safeguarding. Currently, domain experts evaluate the plots by hand. This labor-intensive activity can be mechanized utilizing cutting-edge computer vision (CV) technology. Image-reliant systems, and lately those grounded in deep learning, have demonstrated strong performance across multiple agricultural use cases. These image-based applications surpass specialist appraisals in regulated conditions and are now progressively being incorporated into uncontrolled, in-field deployments. A new solution, rooted in deep learning techniques and image processing, is proposed to estimate crop damage under field conditions. The recommended solution operates through a two-stage pipeline. The first stage identifies individual plants within the plots using a YOLO-based object detection model. After that, a regression model is applied to gauge the damage per isolated plant. The system was designed and corroborated on oilseed rape plants to measure the damage inflicted by the flea beetle. The plant detection model achieves an overall mean average precision of 91%, with [email protected] of 0.99 and [email protected] of 0.91 for the oilseed rape category. The regression model, built to predict damage severity up to 60% in single plants, yields an MAE of 7.11 and an R² of 0.46 when evaluated against plant-by-plant assessments conducted by human experts. Both models are packaged in a Docker container and can be directly queried via a REST API using images captured in the field from a mobile device.

Precise forecasting of watermelon harvest volumes is vital within the agricultural supply chain, since it informs the deployment of farming inputs and streamlines stock and logistical scheduling. Conventional harvest assessment depends predominantly on human effort, rendering it both labour-intensive and slow. To tackle this issue, the present study proposes an algorithmic workflow that leverages unmanned aerial vehicle (UAV) video to identify and count watermelons. The workflow combines You Only Look Once version 8s (YOLOv8s) with image mosaicking and overlapping region division, which supports whole-field watermelon quantity estimation. The detection model, built on YOLOv8s and refined via transfer learning, achieved 99.20% detection accuracy, underscoring its suitability for crop forecasting. The counting strategy, which employs image mosaicking and overlapping region division, accepts stitched panoramas as input and significantly reduces double counting compared to the video-tracking-based detection and counting method. Counting accuracy exceeded 96.61%, indicating strong promise for yield forecasting applications. This elevated accuracy attests to the practicability of deploying the proposed approach for total harvest estimation across expansive watermelon cultivations.