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The light-trap attraction rate (LTARI) constitutes a critical parameter for delineating diel activity profiles and underpins work in insect behavioral ecology and pest suppression. Nevertheless, mainstream automated light-trap apparatuses commonly employ destructive methods (e.g., electrified meshes or thermal irradiation), resulting in substantial losses of non-target organisms and markedly impairing image quality due to carcass accumulation. These shortcomings disturb indigenous assemblages and thwart attempts to measure LTARI. Our central objective is to engineer and appraise a non-lethal recording framework to serve as a technical substrate for subsequent LTARI inquiry, rather than to conduct direct quantitative benchmarking against orthodox traps. To remedy the shortcomings outlined above, we present a live-specimen recording instrument that merges a wind-aspiration collector with a Water-Flow Dispersion and Transport Structure (WF-DTS). The harmless trap–disperse–release pipeline curbs body layering, enables the return of captured creatures to the wild, and yields a community-scale post-collection survival fraction of 94% under the tested regime. Empirical data demonstrate that the prototype safeguards picture coherence featuring unambiguously separated single organisms and realizes a recognition score of 95.6% ([email protected]) employing the YOLOv8s architecture. During inference, only the built-in resizing and standardization routines of YOLOv8s are invoked, without supplementary noise filtering, background removal, or synthetic sample inflation. These findings indicate that the WF-DTS yields snapshots that are inherently more amenable to partitioning and identification than those from legacy equipment. The elevated recognition fidelity is predominantly ascribed to the mechanical splaying of individuals and the homogeneous, pale-matte arena provided by the hardware blueprint. Taken as a whole, the ensemble embodies a harmless hardware–software apparatus apt to trim downstream analytical burdens and provide methodological groundwork for enhanced LTARI calculation in prospective, purpose-built field exercises.

Swarm-based unmanned aerial vehicle (UAV) systems are increasingly recognized for their capabilities in extensive IoT and precision agriculture operations, notably in collaborative surveillance and the tracking of multiple moving objects across outdoor terrains. Nevertheless, prevailing coordination mechanisms tend to assume flawless communication channels, disregarding practical network disturbances including congestion, dropped packets, and transmission delays. Such disruptions hinder the prompt relay of data among UAVs and the terrestrial control station, causing control instructions to arrive late or not at all. The outcome is a breakdown in coordination fidelity and a steep decline in tracking effectiveness when these systems are fielded. To remedy this shortcoming, we put forward CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid governance scheme that fuses centralized learning-powered decision processes with decentralized rule-governed behaviors, enabling UAVs to tailor their actions to instantaneous network status. The CSOOC framework is built around three core elements: (1) an online component providing centralized orchestration while communication remains dependable, (2) an offline, profit-guided movement policy relying on localized Gaussian maps to sustain autonomous target pursuit when connectivity drops, and (3) congestion mitigation rooted in STAR (Stratified Transmission and RTS/CTS), which couples staggered transmission timing with RTS/CTS handshake protocols to bolster uplink robustness. A cohesive co-simulation infrastructure is constructed, linking network-layer communication with swarm-level control and coordination dynamics. Trials reveal that CSOOC delivers a mean observation ratio of 39.7%, outperforming reference algorithms by margins of 4.4%–11.13%, while also boosting network dependability through markedly improved packet delivery success under saturated channel conditions. These outcomes confirm that CSOOC successfully spans the disconnect between algorithm performance within simulators and the operational realities of UAV collectives in communication-challenged settings.

Driven by the worldwide surge in fish maw commerce, authenticity verification has emerged as an urgent concern due to mounting adulteration incidents. Conventional approaches to verification—whether sensory-based, chemically oriented, or molecular—suffer from practical deployment constraints. The present study reframes fish maw genuineness assessment as a detection task within the object recognition paradigm and introduces RDT-YOLO, a streamlined detection model grounded in YOLO11n architecture. Addressing the twin difficulties of insufficient nuanced texture representation and processing overhead in this domain, we engineer hierarchically structured reparameterized modules for feature extraction that deploy reparameterization tactics to amplify multi-resolution texture discrimination. To counteract degradation of information arising during cross-scale feature merging, a Dynamic Adaptive Multi-Scale Pyramid Processing (DAMSPP) component is devised, integrating adaptive convolution dynamics that facilitate responsive feature synthesis. Concurrently, an Adaptive Task-Aligned Detection Head (ATADH) is formulated, fusing interactive task mechanisms with weight-shared convolutions to achieve parameter economy alongside precision gains. Moreover, Wise-ShapeIoU, a novel loss formulation, is constructed by injecting Wise-IoU's focusing coefficient into the Shape-IoU framework, thereby sharpening detection fidelity through refined contour alignment. Benchmark experiments confirm that RDT-YOLO delivers precision of 91.9%, recall of 89.6%, and [email protected] of 94% while slashing parameter count, storage footprint, and FLOPs by 75.6%, 73.8%, and 63.8%, respectively, against YOLO11s. Comparative trials versus YOLOv10s and YOLOv12s reveal [email protected] elevations of 0.8 and 0.5 percentage points. This investigation furnishes an automated pipeline for verifying fish maw genuineness, bearing implications extending across food integrity applications.

Crop diseases constitute a danger to worldwide food stability, diminishing harvest yield, grade, and total farming efficiency. Conventionally, the identification of plant ailments has depended on lengthy visual assessments by specialists, a method that frequently results in mistakes. Machine learning (ML) and artificial intelligence (AI), in particular Vision Transformers (ViTs) and Convolutional Neural Networks, deliver a swifter, automated substitute for recognizing plant illnesses through leaf image analysis. However, these frameworks are regularly faulted for their "black box" character, which curbs confidence in their outputs due to a lack of clarity. Our outcomes indicate that integrating Explainable AI (XAI) approaches like Grad-CAM, Integrated Gradients, and LIME notably boosts model explainability, rendering it simpler for users to detect the fundamental indicators of plant sicknesses. This investigation not only adds to the area of plant disease recognition but also supplies an original viewpoint on advancing AI openness in actual farming settings through the application of XAI methods. With training accuracy rates of 100.00% for ViT, 96.88% for EfficientNetB7, 93.75% for EfficientNetB0, and 87.50% for ResNet50, and equivalent validation accuracy rates of 96.39% for ViT, 86.98% for EfficientNetB7, and 82.00% for EfficientNetB0, our suggested architectures exceed earlier studies performed on the identical dataset. This illustrates a considerable leap in model capability while upholding clarity and dependability by way of explainable and trustworthy decision procedures.

Food waste is a critical concern for both developing and advanced economies alike. Digital technologies hold promise for helping to alleviate this problem, and our investigation sets out to deliver a workable prototype employing cloud-based image classification alongside tailored OCR techniques. The product of our work is an economical yet high-performing mobile app prototype that establishes a foundation for future inquiry. We drew on sophisticated principles of application integration—encompassing mobile frameworks, Firebase machine learning modules, and OCR procedures—to signal how near food items are to spoiling. Where no printed date exists, the system generates an indicative shelf-life projection built on prudent category-based assumptions. These projections do not constitute safety determinations and are not a substitute for manufacturer date markings or national food-safety regulations. The outcomes lend our paper unmistakable originality and scholarly value, boosting the operational efficiency of storage facilities and retail food outlets. The ramifications of our work are technical, economic, and societal in nature.

The increasing menace of climate change constitutes a profound obstacle for contemporary farming, carrying grave implications for worldwide food availability. The effect of altering climatic parameters on agricultural output, particularly for essential staple commodities, fuels worry regarding prospective harvests. This investigation explores the likely repercussions of climate change on cotton cultivation by merging archival climate records, Global Climate Model (GCMs, CMIP3) forecasts, and cotton production statistics. A broad spectrum of machine learning (ML) methodologies is applied, including multiple regression, k-nearest neighbors (KNN), boosted tree techniques, and several variants of artificial neural networks (ANNs), to delve into the intricate linkage between climatic drivers and cotton productivity. The models are built and verified using climatic and crop output data obtained from three districts within Punjab, Pakistan, across the timeframe 1991 to 2020. To anticipate future production outcomes, climatic projections from General Circulation Models (GCMs) are refined via downscaling under the SRA1B, A2, and B1 emission trajectories, facilitating outlooks extending to 2050. The outcomes reveal that precipitation exerts a trivial influence on cotton output (R = 0.0002), while peak temperature (R = -0.183) stands out as the foremost climatic determinant shaping yield, trailed by lowest temperature (R = 0.248). Of the tested architectures, the generalized feedforward (GFF) network delivered the optimal results (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), exceeding the performance of the probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. By comparison, linear regression (LR) and multiple regression formulations registered weaker predictive power. The trustworthiness of GFF and KNN for generating yield projections (R² = 0.892, 0.861) endorses their suitability for reliable forecasting. The study calculates a 4.5% contraction in cotton harvest by 2050, measured against the peak recorded yield for the locality, stressing the footprint of climate change on cotton farming and its latent hazard to food supply. Nonetheless, the flexible strength of the ANN (GFF) frameworks under varying climate storylines delivers auspicious means for weaving ML into climate-adaptive cultivation regimes, advancing durable agrarian safety and curbing the detrimental fallout of climate shifts on nutrition provision.

Reliable estimation of crop yield is vital for refining farming operations and guaranteeing food supply. This investigation tested the capacity of machine learning techniques to estimate tomato yield using meteorological measurements, spectral reflectance bands, and vegetation indices subjected to different nitrogen levels and bio-stimulant applications to generate variation in crop development. UAV-mounted sensors captured spectral information across seven acquisition dates spanning October 27 to December 15, 2023, matching critical developmental windows: vegetative establishment (acquisition 1), anthesis (acquisitions 2 and 3), fruit set and enlargement (acquisitions 4, 5, and 6), and initial maturation (acquisition 7). Discriminating explanatory variables were selected via the Pearson correlation metric (r > 0.65, P < 0.05), incorporating Near Infrared (NIR), Red Edge, and Red wavebands, together with vegetation indices comprising NDVI, GNDVI, NDRE, and SAVI. Remotely sensed canopy signatures gathered throughout the fruit set and enlargement window (acquisitions 5 and 6) displayed the tightest association with harvested yield (r = 0.66–0.74), reinforcing the necessity of spectral intelligence from the middle to latter parts of the cycle. When comparing predictive algorithms, both linear regression (LR) and XGBoost yielded the strongest predictive skill, producing root mean squared error (RMSE) figures of 16.13 kg and 16.15 kg, respectively, matched with R² values reaching 0.63. Support vector machine (SVM) and decision tree (DT) implementations also delivered competent outcomes, achieving RMSE scores of 17.15 kg and 17.18 kg, respectively. By comparison, the deep learning architecture lagged notably (RMSE = 23.49 kg, R² = 0.23), plausibly attributable to data scarcity. This research spotlights the prognostic capability inherent in spectral wavebands and underscores the centrality of growth-stage-aligned spectral acquisition for yield forecasting.

Real-time tracking of soybean seedling development demands an efficient approach for correctly recognizing seedlings and eliminating weeds. Difficulties stem from the tiny scale and shape-based likeness between seedlings and weeds, which makes standard detection techniques more complex. In response to these issues, a real-time detection framework termed YOLOv9-GSSA is introduced. A refined Mosaic-Dense algorithm boosts object concentration at the input stage of the model, improving its power to capture subtle features. Additionally, a neck optimization component called GSSA, integrating GSConv with Gated Self-Attention, aids essential information extraction and the exchange of features across scales. The Swin-GSSA prediction head further draws on spatial positioning cues, enabling better small object recognition and resolution of overlapping occlusion. Tests reveal that our model secures a mAP of 47.5% with a per-image detection speed of 23.42 ms, making it viable for real-time surveillance. The upgraded model notably strengthens the recognition of soybean seedlings and weeds, functioning as a useful asset for efficient farmland oversight. This ultimately contributes to precise yield forecasting and informed choices in precision agriculture.

In the dairy industry, maintaining the welfare of every cow and curbing financial losses requires individual-level surveillance, which is achieved through cow Re-Identification (Re-ID). Computer vision-based Re-ID hinges on visually distinguishing characteristics, such as the specific coat patterns seen in breeds like Holstein. However, manually labelling every cow across all farms is financially unfeasible. We aim to formulate Re-ID strategies that work on both annotated and non-annotated farms, accommodating new individuals and varied settings. Unsupervised Domain Adaptation (UDA) approaches aim to close this divide by transferring knowledge from labelled source domains to unlabelled target domains. However, they have largely been tailored to person and vehicle Re-ID scenarios. Our research proposes Cumulative Unsupervised Multi-Domain Adaptation (CUMDA) to address the challenges of constrained identity variation and dissimilar farm appearances. CUMDA gathers knowledge across all domains, reinforcing specialization in familiar domains while strengthening generalization to unknown ones. Our offerings include a CUMDA technique that adapts to multiple unlabelled target domains while safeguarding source-domain efficacy, along with thorough cross-dataset testing across three cattle Re-ID datasets. These tests reveal substantial gains in source conservation, target-domain specialization, and adaptation to unseen domains.

Forest health is under mounting pressure from diseases driven by invasive pathogens, and the ability to detect these diseases early and precisely is vital for targeted, prompt forest management. Breakthroughs in spectral imaging and artificial intelligence have paved the way for novel approaches to plant disease identification in both crop and tree species. The present study employed the Dutch elm disease (DED; caused by Ophiostoma novo-ulmi) and American elm (Ulmus americana) pathosystem as a model to test the efficacy of two custom-fabricated, high-precision, portable hyper- and multispectral leaf scanners, paired with machine learning, as innovative instruments for forest disease surveillance. Hyper- and multispectral imagery was collected from leaves of American elm genotypes with varying degrees of disease susceptibility following mock inoculation and inoculation with O. novo-ulmi in controlled greenhouse conditions. Both classical machine learning and cutting-edge deep learning architectures were trained on extracted spectra and directly on spectral image volumes. Deep learning frameworks that leverage combined spectral and spatial information from high-definition spectral leaf images surpass classical machine learning approaches that rely exclusively on spectral data for DED identification. Within the deep learning framework, leaf margins and symptomatic lesions were accentuated as discriminative spatial cues for separating leaves originating from inoculated versus mock-inoculated specimens. Furthermore, the spectral and spatial signature profiles uncovered by the machine learning-based models corresponded to the DED susceptibility levels of the elm genotypes. While supplementary investigations are warranted to gauge transferability across other pathosystems, hyper- and multispectral leaf scanners integrated with machine learning show considerable promise as emerging tools for disease phenotyping in tree species.

Vegetation cultivated in aquaponics environments is prone to multiple diseases or biotic pressures throughout its development, similarly to conventional farming. Timely identification of these diseases is essential to maintaining the effectiveness of the aquaponics setup and advancing its development. Aquaponics merges recirculating aquaculture with soilless hydroponic cultivation and has the potential to ensure food security, alleviate water shortages, and reduce carbon emissions. To facilitate the widespread adoption of this agricultural method, an integrated platform is required to identify plant ailments and assist scientists and growers in identifying potential causes and remedies at an early stage. This research introduces an automated plant diagnostic framework for recognizing biotic pressures and controlling diseases in four leafy green vegetables—lettuce, basil, spinach, and parsley—cultivated within an aquaponics environment. Initially, a collection of 2640 images is assembled. Subsequently, a disease identification system operating in three stages is created. The initial stage involves a crop classification mechanism that determines the plant species. The second stage comprises a disease identification mechanism that assesses the plant’s health condition. The concluding stage is a disease-detection mechanism that pinpoints diseased and healthy areas on foliage and classifies the illness. The suggested method has yielded encouraging results, achieving accuracies of 95.83%, 94.13%, and 82.13% in the three stages, respectively. The ultimate disease-detection mechanism is then combined with an ontology framework via a cloud-based interface. This ontology framework includes domain expertise concerning plant pathology, specifically the origins and remedies for various ailments of the examined leafy green vegetables, which can be automatically retrieved upon illness detection, enabling farm operators to implement preventive actions. The suggested application is important as a decision-support tool that automates health surveillance in aquaponics facilities and helps agricultural practitioners make decisions on crop and disease control.

Contemporary swine husbandry falls short in operational effectiveness, as current systems depend largely on electromechanical controls and can only classify pigs by body mass. Such a method is not only inefficient but also increases labor overheads and the risk of zoonotic infections. Additionally, confining pigs in large herds can worsen disease transmission and make the observation and treatment of unwell animals more difficult. This investigation conducted a trial to develop a deep-learning categorization mechanism, drawing on a dataset enriched with essential metrics and farm imagery amassed over 24 months. The study utilized a Kalman filter-based algorithm to boost the precision of the dynamic categorization process. The experiment introduced a first-of-its-kind machine vision classification system, powered by deep learning, capable of processing live images for multifaceted recognition. The individual recognition model, built on a Residual Neural Network (ResNet), predicts livestock mass using ongoing data. In contrast, the Wasserstein Generative Adversarial Nets (WGAN) image enhancement algorithm improves recognition across diverse conditions, thereby strengthening the model’s resilience. Crucially, the system can detect livestock exhibiting signs of likely illness via atypical body language and sequester them for protection. Experimental results confirm the advantage of the introduced system over standard alternatives. It not only reduces manual intervention and data maintenance costs but also improves the accuracy of livestock recognition and better uses data. The outcomes demonstrate an 89% success rate in livestock ID recognition, a 32% increase in blocked-image recognition, a 95% surge in livestock categorization precision, and a striking 98% success rate in distinguishing pictures of sick pigs. In essence, this research enhances identification efficiency, reduces operating costs, and provides superior tools for disease surveillance.

Accurate identification of rice diseases in precision agriculture using imagery remains difficult due to substantial misclassification rates, a wide range of influencing factors, and fluctuating conditions. While machine learning together with convolutional neural networks has yielded encouraging results for detecting rice diseases, these approaches have struggled to elucidate the interconnections among disease characteristics. In this work, we introduce an enhanced classification framework for rice diseases that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit (BiGRU). Concretely, a residual connection was embedded into the Inception module, its depth was enlarged, and a refined Convolutional Block Attention Module (CBAM) was incorporated. The upgraded CNN and the BiGRU were trained and evaluated separately; their outputs were then concatenated and forwarded to the classification layer for final prediction. Trials confirm that this strategy achieves 98.21% classification accuracy across four rice disease categories, delivering a robust solution for rice disease detection research.

An efficient pipeline for sorting clusters of touching corn kernels—potentially containing sound grains, defective grains, and foreign matter—is introduced. The method operates in two steps. First, a next-generation instance segmentation network, trained entirely on a corpus of synthetically generated images, separates the input scene into distinct individual objects. Second, a purpose-built, lightweight CNN architecture assigns each segmented object to one of three categories (sound, defective, or foreign matter). The segmentation model is learned using a strategy that eliminates the need for labor‑intensive, subjective manual annotation. For the classification step, the custom network is deliberately constructed with a minimal number of layer blocks, yielding a compact architecture suitable for on-device deployment. Experimental evaluations and comparative benchmarks confirm gains in both recognition accuracy and processing speed. The proposed segmentation and classification framework can be readily transferred to other cereal grains.

Computer vision and deep learning methods have attracted substantial interest in agriculture owing to their contactless, non-destructive nature. These technologies are increasingly being embedded in modern farming platforms, such as aquaponics, to tackle the barriers limiting their commercial expansion and industrial-scale deployment. Aquaponics is a production technique that fuses a recirculating aquaculture setup with soilless hydroponic cultivation, holding promise for addressing food security challenges. To supplement existing investigative efforts, a procedure is proposed to automatically quantify crop morphological features—namely, width, length, and area—and to infer the effective planting distance between grow channels. Planting distance is a core design variable that depends on crop type and its morphological expression; it therefore requires continuous surveillance to safeguard high yield and quality, which can be compromised by canopy occlusion or overlapping as plants develop. The presented pipeline applies Mask R-CNN to estimate crop dimensions and a mathematical formulation to determine plant spacing tailored to a self-adapting aquaponics installation. For common little gem romaine lettuce, growth is predicted within a 2 cm deviation for both length and width. The finalized model is served via a cloud-based platform and is coupled with an ontology model that encodes domain expertise in aquaponics systems. Pertinent knowledge regarding crop traits and ideal planting intervals is drawn from the ontology and cross-checked against the model’s outputs to propose follow-up measures. The introduced application derives its value as a decision-support framework, potentially paving the way for intelligent oversight and regulation.

Properly managing water application in urban plantings is essential throughout arid and semi-arid regions and depends upon consistently accurate calculations of reference evapotranspiration (ETo). The meteorological observations obtainable in city landscapes, however, are rarely sufficient to apply the standard Penman-Monteith methodology for ETo derivation. Intelligent landscape irrigation controllers thus often rely on temperature-driven ETo approximations to automate watering schedules. The present study is directed at crafting deep learning temperature-based ETo predictors and contrasting their accuracy against two widely adopted empirical temperature-based formulas, specifically the FAO Blaney and Criddle (BC) and Hargreaves and Samani (HS). A complementary, freely available, and intuitive software utility, DeepET, was also developed based on the highest-performing deep learning predictors produced in this work. Four classes of artificial neural network (ANN) predictors were assembled, one group operating on raw weather observations and a second group utilizing wavelet-transformed reconstructed signals as input features. Additionally, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolutional neural network (CNN) predictors were built. The dataset underpinning this work was sourced from 101 operational California Irrigation Management Information System (CIMIS) weather stations, yielding more than 725,000 data points spanning 1985-2019. All predictors were benchmarked against the officially reported CIMIS ETo. When tested on previously unseen station records, the temperature-fed DL (Deep Learning) predictors yielded mean absolute errors that were 15%–20% lower than those from the locally calibrated HS equation. Feeding reconstructed wavelet-transform signals into the ANN predictors did not yield any measurable gain. Our findings suggest that DL-based predictors constitute a compelling approach to improving ETo accuracy in urban settings when only temperature readings are available. The DeepET utility is reachable from the Haghverdi Water Management Group portal: http://www. ucrwater.com/software-and-tools.html.

Grain buyers reject canola kernels that have been heated or remain immature due to excessive drying or frost exposure, as these defects reduce both oil extraction rates and commercial value. The current grading protocol relies on visually inspecting each seed's endosperm hue and manually tallying the damaged specimens. Given that this method is tedious, labour-intensive, and prone to human error, the present work proposes an automated grading pipeline based on object detection, multi-object tracking, and enumeration. The detection phase was accomplished using a refined YOLOv7 architecture (YOLOv7_ours), which was adapted to bolster its small-object recognition accuracy by embedding two convolutional block attention modules into the neck section, while simultaneously lowering its computational demand (cost) and footprint by swapping conventional convolutional layers for ghost layers across all Efficient Layer Aggregation Networks modules and within the Spatial Pyramid Pooling Cross Stage Partial module native to YOLOv7. The trained network parameters were then passed to the ByteTrack multiple-object tracker, enabling frame-by-frame tracking of detections throughout a video sequence. The distinct identifiers generated by the tracker for each target of interest were subsequently used to tabulate defect counts using a line-crossing procedure. Following training, the YOLOv7_ours model delivered a mean average precision ([email protected]) that was 1.02% superior, alongside a 32.1% reduction in cost and a 37.1% reduction in size relative to the baseline YOLOv7 model. Assessed on a test video, the full pipeline achieved a multi-object tracking accuracy of 84.8% and a counting accuracy of 93.9%. This three-component framework is ready for integration into edge devices, enabling grain buyers to precisely grade canola kernels in real time.

In agricultural production and yield, product quality certification is a critical procedure. Older methodologies for grading seed quality suffer from drawbacks such as overly complex workflows, limited accuracy, and slow inspection rates at high output volumes. Automated sorting methods based on computer vision and machine learning offer swift, high-capacity alternatives. Even with the substantial breakthroughs in modern automatic classification models, there remains a need to advance these systems by integrating other approaches. This study details the creation of a computer vision platform for autonomously sorting diverse seed types. It relies on machine learning frameworks and data mining procedures, using a collection of geometric descriptors of bean seeds derived from binary imagery. A comparison was drawn among three machine learning strategies: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The workflow also included Principal Component Analysis (PCA), hyperparameter optimization within machine learning algorithms, and dataset rectification using the Synthetic Minority Oversampling Technique (SMOTE). Findings revealed that data mining operations—namely Principal Component Analysis, hyperparameter optimization, and the SMOTE approach—improve the quality of classification outputs. The KNN classifier delivered the strongest showing, attaining close to 95% accuracy alongside 96% precision and recall. The top-tier outcomes were achieved by performing hyperparameter optimization and applying the SMOTE method during data preparation, resulting in a gain of roughly 2.6%. The results validated that integrating data mining into the preprocessing stage and machine learning classification approaches can effectively and swiftly improve sorting accuracy and facilitate automated selection of bean seeds from digital images. This assists small-scale growers and agricultural overseers in making decisions about seed selection to increase yields.

Global food security is critically threatened by plant diseases, which can be especially catastrophic for smallholder farmers whose economic survival hinges on a productive groundnut crop. It is therefore vital to engineer an algorithm capable of early, automated plant disease diagnosis. Working alongside a pathologist, a large-scale dataset of groundnut leaf imagery was assembled to enable this automated detection. In image classification tasks, Convolutional Neural Networks have demonstrated remarkable efficiency. This investigation constructs a disease identification and classification system using a tri-CNN architecture that incorporates DenseNet169, Inception, and Xception, all of which were initially trained on the ImageNet database. The proposed ensemble strategy formulates final predictions for test data by applying two custom nonlinear functions to the decision outputs of these base learners, integrating their scores across four standard performance measures: recall, precision, accuracy, and F1-score. The tailored CNN models are thus utilized to train our system for superior results. The methodology was tested on a real-world groundnut leaf dataset. The introduced model registered an accuracy reading of 98.46%.

Mango fruit exists in multiple cultivar types, and the principal maturity markers for quality assessment are total soluble solids (TSS) and pH. The investigation assessed the viability of coupling a handheld NIR spectrometer (NIRS) covering 740-1070 nm with multivariate algorithms and a smartphone to classify mango cultivars and assess their ripeness. A batch of 198 whole mango fruits was scanned using the NIR device; concurrently, a digital refractometer and a pH meter were used to quantify TSS and pH from extracted juice. Following various spectral preprocessing treatments, multivariate classification models—support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), neural network (NN), and a novel hybrid classifier (LDA-SVM)—were developed for varietal discrimination. Quantitative models predicting TSS and pH were constructed using partial-least-squares regression variants, including interval partial-least-squares (IPLS), synergy partial-least-squares (Si-PLS), and back interval-partial-least-squares (Bi-PLS). For classification, RAW, MC, SNV, FD, and SD preprocessing paired with the LDA-SVM classifier delivered perfect (100 %) varietal recognition in the training set and 97.44 % accuracy in the prediction set. For quantification, the optimal prediction model for both TSS and pH was Si-PLS, achieving R² values of 0.63 and 0.81, respectively and RMSEP values of 1.83 and 0.49, respectively. The study proved that swift, non-invasive estimation of TSS and pH is feasible by integrating a handheld NIR sensor with suitable chemometric analyses.

The evaluation of plants in greenhouse environments is an essential phase in the development and assessment of new herbicides, as it enables controlled examination of species responses to different formulations and concentrations. To this end, experiments are performed in greenhouses where the injury inflicted on treated plants is monitored daily. Each pot is typically assessed by comparing it against an untreated reference pot, designated as a control. Presently, this task is carried out pot by pot via a time-intensive procedure reliant on visual judgments made by experienced specialists. Digital instruments are needed to shorten the time required and to equip specialists with approaches that are both more objective and more repeatable for quantifying plant injury. A novel solution, grounded in image processing and deep learning, is introduced to estimate plant injury across different developmental stages in the greenhouse. Plants are exposed to multiple injury types and phases, and photographs of them are taken to assemble a dataset. The accessible annotation corresponds to the injury estimation score supplied by the specialists. The proposed framework attempts to replicate the way specialists assess plant injury through a two-stage process. First, the reduction in biomass of the evaluated plant relative to its control plant is quantified; second, potential disease markers on the plant are identified. The former step uses classical image processing methods, while the latter relies on a deep learning-based multi-label classification model for symptom recognition. The algorithm has been validated on two species: Glycine max (soybean) and Amaranthus retroflexus (redroot amaranth). Coefficients of determination (R²) of 0.87 and 0.89 are attained, respectively, for injury estimation. The approach enhances both the efficiency and the objectivity of the current manual workflow.

Agricultural production is affected by plant diseases, affecting both quality and quantity. Yet, forecasting these diseases demonstrates its value for crop quality and reducing the chance of yield losses. The process of detecting plant diseases—whether by the naked eye or through conventional methods—is generally painstaking, time-consuming, and subject to substantial margins of error. This work presents a comprehensive exploration of diverse CNN architectures paired with distinct optimization algorithms, applied to olive disease identification via classification methods, and identifies the optimal model for an efficient disease detector. The exploration presents a collection of 5571 olive leaf images, gathered by hand in field settings across different regions of Morocco, and additionally includes a healthy class to identify olive diseases. Moreover, a primary aim of this research was to explore the interplay between CNN architectures and optimization algorithms as measured by accuracy and additional performance indicators. The peak score in the trained models reached 100%, while the peak score in experiments without data augmentation was 92.59%. A further emphasis of this exploration is the effect of optimization algorithms on neural network functioning. Based on the experiments, the MobileNet architecture with Rmsprop outperformed the other pairings in both detection performance and efficiency.

In contemporary farming, visual perception systems grounded in deep learning are emerging to equip autonomous vehicles with the capability to perform crop-field interventions. Yet securing strong performance from these techniques hinges on access to large volumes of data, a resource that is typically scarce in agricultural contexts. The core reason is that constructing an agricultural dataset that captures a given problem in exhaustive breadth is difficult, since the visual presentation of symptoms is subject to change. There is pronounced sensitivity to ambient variables such as heat, dampness, and lighting regimes. An efficient pipeline is therefore imperative to methodically shepherd the full sequence of building an agricultural dataset, from initial image capture through to its web-facing release. This manuscript describes the Eden Library, a platform conceived to help close the recognized shortfall in freely available crop and plant repositories that span both close-range and overhead imagery. The end-to-end workflow underpinning the platform’s conception and launch is also set forth and explored. This workflow merits attention because the datasets on offer are designed not as static, one-off research outputs confined to a narrow slice of species, growth stages, and environmental circumstances, but rather as evolving resources to be stewarded and expanded across time. Annotations delineating plants and their associated symptoms are supplied, thereby relieving users of the manual labeling workload. At the time of writing, the Eden Library spans 15 distinct crop species, 9 weed varieties, and 30 forms of disorder (encompassing pests, diseases, and nutritional shortfalls). The Eden Library endeavors to narrow this divide by delivering a voluminous, richly varied image trove of plants, curated coherently, to accelerate the development of future vision-driven, AI-augmented field tools.

In light of ongoing trends in metropolitan farming and global climate shifts, there is a growing need for alternative plant cultivation strategies that can operate independently of soil. Aquaponic and hydroponic growing methods have proven workable replacements, yet the scarcity of streamlined, ideal procedures for watering and nutrient delivery curtails their deployment at a mass commercial scale. The primary objective of this investigation was to develop statistical techniques and Machine Learning models to adjust nutrient levels in aquaponic irrigation systems in response to crop demand, thereby securing peak plant performance and facilitating wider commercial uptake of aquaponic farming. A core difficulty in crafting these models is the thinness of available data, which calls for the application of Bolstered error estimation methodologies. This paper assesses a range of linear and non-linear algorithms conditioned on comparatively small data sets using bolstered error estimation techniques to identify the superior approach for guiding nutrient control decisions in hydroponic contexts. Upon conducting multiple tests on the data, it was concluded that the Semi-Bolstered Resubstitution Error estimation strategy yields the best results in our scenario, using a Linear Support Vector Machine as the categorizer with a penalty factor of 1. A series of suggested directives was developed as a Decision Support System, derived from the Machine Learning model’s outputs, and verified against the baseline framework’s findings. Moreover, the constructive influence of the recommended nutrient mixtures on plant growth in aquaponic conditions is examined in detail.

Leaching losses of nitrogen (N) from grazed pasture are driven predominantly by urinary N deposited by cattle. This work aimed to use non-invasive accelerometer-based sensors to capture the timing and duration of urination and defecation events in grazing cattle. To register the back arching motion that accompanies urination and defecation, each cow was fitted with two accelerometer sensors. Experiments took place outdoors under grazing conditions during two seasons—autumn and summer—and captured a total of 160 urination events. Across the majority of these events, the tilt angle was observed to shift within a range of 8 to 22.5 degrees. Using 18 selected features that characterise a cow's back arching, ensemble machine learning classifiers were constructed to recognise urination and defecation. The urination classifier delivered an F1 score of 0.80 (harmonic mean computed from a precision of 0.82 and a sensitivity of 0.78). In contrast, the defecation classifier gave an F1 score of 0.73 (calculated from 408 hours of recordings made between 9 a.m. and 3 p.m. from 19 cows). Predictive models for urination event duration were statistically significant. Still, they underperformed, with low explanatory power, in part because the relationship between the duration of back arching and the duration of urine passage was only moderate (R² values of 0.29 in autumn and 0.47 in summer). The work highlights that non-invasive accelerometer sensing has real promise for capturing individual cows' urination patterns and for generating animal-level information that farm decision support systems (such as those addressing urine-N excretion) and managerial actions (such as selective culling or breeding) could use as input.

A variety of techniques have been put forward for measuring the straight-line precision of an autopilot-driven tractor as it moves through agricultural fields. This investigation presents an alternative strategy for gauging the straight-line precision of an autopilot tractor traversing oil palm plantation landscapes. The strategy was constructed by integrating mapping software with spreadsheet software. The tractor’s embedded GPS unit charted its passes across the oil palm field. The documented tractor passes, together with their World Geodetic System (WGS) 1984 latitude and longitude coordinates, were reprojected into the Kertau Universal Transverse Mercator (UTM) system on a Cartesian XY-plane using mapping software. The reprojected coordinate data were then brought into a spreadsheet application to calculate the deviations in straight-line precision. This alternative strategy successfully identified a mean straight-line precision error of ±7.78 cm for the tractor moving on oil palm terrain. Broadly, this strategy holds considerable promise for complementing established techniques for appraising straight-line precision of autopilot tractors in fieldwork.

Forecasting crop yields provides crucial intelligence for growers of high-value commodities, helping them organize and streamline harvest logistics. The bulk of current yield forecasting approaches rely on manual sampling and statistical procedures, which are intricate, labor-intensive, and time-consuming. Lately, advances in image processing and the broader integration of autonomous systems, such as unmanned aerial vehicles, have enabled swifter, more precise yield forecasting across diverse crops. This work puts forward a novel, rapid, efficient, and accurate image processing algorithm. The segmentation technique proved capable of distinguishing mature citrus trees from their natural surroundings within the CIE (Commission Internationale de l'Éclairage) Lab color space. The influence of the spacing between the camera and the target on resolution and detection accuracy was examined and discussed. Outcomes revealed that distances from 1.524 meters (5 feet) up to 2.134 meters (7 feet) yielded high detection accuracy. The count of correctly recognized fruit attained 91.69%, with an average processing duration of 1.1 s for each image across a set of 132 images. A correlation coefficient (R2) equal to 0.98 was secured between the citrus counting algorithm and counts generated through human visual assessment of 66 sample trees. A comparative review of the introduced technique indicated greater accuracy and robustness than other related research.

China’s rapid socioeconomic transformation, marked by urbanization and land-use shifts, is creating novel contexts for the implementation of precision agriculture (PA). Two PA approaches were tested in a single-year experiment to evaluate their economic returns: precision seeding applied alone and precision seeding paired with land leveling. Although the trial produced favorable results, it could not, by itself, confirm longer-term advantages. The outlay for land leveling is concentrated in a single year, whereas its benefits may persist for multiple years. Therefore, if this PA practice yields benefits that extend into future seasons, the economic appraisal would improperly concentrate all expenditures in a single year of returns. In other words, the benefit-cost ratio would be underestimated. To determine whether benefits carried forward, we employed NDVI and GUI as proxy measures for gains in subsequent years. From the single-year experiment, our results demonstrated that: (1) Winter wheat output climbed by 23.2% when precision seeding and laser leveling were jointly applied. (2) Used in isolation or combination, the technologies markedly lowered soil ammonium nitrogen levels at the 60 cm depth; (3) The treatment benefit/cost ratios outperformed the baseline by roughly 10%, equating to an increase of several hundred US dollars per hectare. NDVI analysis indicated that the influence of laser land leveling persisted for up to 2 years. Factoring in the multi-year effect, the benefit/cost ratio of PSLL climbs to 23.5% and 22.9%, depending on whether laser land leveling subsidies are included. Elucidating the economic benefits of PA technology adoption should facilitate its broader adoption across the region.

In recent years, the deployment of artificial intelligence (AI) in farming has emerged as a critical priority. Embracing technological innovation in agriculture, when pursued imaginatively, unlocks significant value. A vital procedure involves managing affected leaves throughout crop cultivation. The prompt identification, categorization, and examination of foliar diseases, together with viable remedies, perpetually foster agricultural progress. A central emphasis of our proposed study is the detection and categorization of diseases across different crops, specifically targeting tomatoes and grapes. The principal aim is to anticipate the type of disease likely to affect grape and tomato foliage at an early stage. Techniques employing Convolutional Neural Networks (CNNs) are harnessed for Multi-Crop Leaf Disease (MCLD) detection. A deep learning-centric model carried out image feature extraction to differentiate between infected and vigorous leaves. The CNN-dependent Visual Geometry Group (VGG) model is applied to achieve superior performance indicators. A collection of crop leaf imagery serves to both train and evaluate the model. Metrics gauging performance, including accuracy, sensitivity, specificity, precision, recall, and the F1-score, were determined and observed. The overarching research aim with the proposed framework is to secure steady enhancements in outcomes. The engineered model distinguishes leaves impacted by disease with heightened precision. In the experimental setup, the suggested investigation achieved 98.40% accuracy for grapevines and 95.71% for tomato plants. This body of work directly contributes to boosting agricultural food output.

Evaluating the physical appropriateness of the terrain is an essential condition for increasing harvest productivity and allows farming populations to situate the appropriate crop in the appropriate site. Across Ethiopia, maize ranks among the fundamental staple foods and is produced throughout three agro-ecological belts: the highlands, midlands, and lowlands. Despite these realities, maize output remains critically depressed due to a lack of know-how and informational gaps regarding terrain fitness. Ascertaining the physical suitability of land for maize cultivation is indispensable to mitigating food insecurity. The current inquiry aims to assess the prospective terrain’s suitability for maize cultivation within the Didessa watershed of Western Ethiopia by deploying Multi-Criteria Evaluation (MCE) alongside geospatial tools. Shifts in land use and land cover (LULC), climatic variables, relief, edaphic characteristics, and infrastructural provisions were factored into the terrain fitness appraisal for maize. The pairwise comparison matrix grounded in MCE was applied to approximate terrain fitness for maize crop farming. The findings signified that roughly 977.7 km2 (14.1%) is rated as highly suitable, 4794.9 km2 (69.1%) as moderately suitable, whereas 1118.8 km2 (16.1%) and 51.5 km2 (0.7%) of the examined region were grouped as marginally and not suitable for maize growing, respectively. This inquiry provides vital intelligence for decision-making bodies and the cultivator community to identify prospective zones for maize farming.