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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.

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.

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.

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 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.

Achieving sustainability and efficiency in maintaining high crop yields while minimizing agricultural chemical pollution requires precise weed mapping to fully deploy site-specific weed management—a persistent, major hurdle in contemporary farming. This investigation tested the resilience of training epochs for the You Only Look Once (YOLO) v5s convolutional neural network (CNN) model in building an automated system for classifying crops and weeds from UAV-acquired imagery. The images were labeled with bounding boxes and trained on Google Colab across 100, 300, 500, 600, 700, and 1000 epochs. The network identified and sorted five distinct categories: sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. Training proceeded over multiple epochs to pinpoint the peak test-set performance and was terminated once the test metrics (overall accuracy, precision, and recall) began to decline. The findings reveal a marked improvement in classifier capability as the epoch count climbed from 100 to 600. However, a modest reduction surfaced at 700 epochs, where overall accuracy, weed precision, and recall registered 65, 43, and 43%, respectively, contrasted with the 67, 78, and 34% achieved on these same metrics at 600 epochs. This erosion continued at 1000 epochs, producing an overall accuracy of 65%, a weak precision of 45%, and a recall of 40%. These outcomes confirm that the training epoch length in YOLOv5s substantially governs the model’s reliability for automated crop and weed discrimination, singling out 600 epochs as the optimal performance point.

In some Asian countries, jute is a fundamental source of revenue and contributes to Gross Domestic Product (GDP). Like other crops, jute is vulnerable to pest invasions, and detecting them often relies on visual inspection in countries such as Bangladesh, India, Myanmar, and China. This practice is also slow, difficult, and somewhat inaccurate, posing a significant economic risk. To mitigate this challenge, the investigation proposes a highly accurate and resilient transfer learning (TL)- based JutePestDetect model for detecting jute pests at their initial stages. To begin with, we assembled a jute pest collection comprising 17 categories and roughly 380 images per category, which were scrutinized after manual and automated preprocessing and refinement, including background removal and scaling. Following this, five distinguished pre-trained architectures—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were picked from an earlier work to formulate the JutePestDetect model. Every architecture was adjusted by replacing the classification layer with a global average pooling layer and inserting a dropout layer for regularization. For performance measurement, a range of indicators, including precision, recall, F1 score, ROC curve, and confusion matrix, were used. These examinations provided a deeper understanding of the models’ capabilities. Within this set, the proposed JutePestDetect model, based on a tailored regularized DenseNet201, outperformed its counterparts, achieving an outstanding accuracy of 99%. Hence, our suggested method and tactics deliver a refined solution for pest recognition specific to jute, with the potential to substantially assist cultivators worldwide.

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.

This paper delivers an interview-based investigation and literature analysis examining the potential benefits and barriers to adopting Artificial Intelligence (AI) in farming enterprises. Three branches of agriculture are examined: crop cultivation, dairy farming, and beef farming. As groundwork for the study, a literature analysis re-evaluates earlier work on intelligent farming. Subsequently, an interview study with 21 participants examines receptivity and readiness for intelligent farming tools and provides technical detail on three selected AI applications in agriculture. Though centered on circumstances in Sweden, the results apply to the wider European agricultural context and beyond. The study uncovers a broad range of elements that both promote and hinder the transition. Chief among the recognized opportunities are the strategic weight intelligent farming carries on the agendas of numerous sector players, the overall shift toward software-as-a-service models through co-owned equipment, the enormous stockpiles of existing data, and the substantial eagerness among farmers for novel technology. Conversely, the study highlights primary obstacles, including technical and legal difficulties related to data ownership, looming cybersecurity threats, the need for a clearly articulated economic rationale, and the occasional lack of technical competence within the sector. The paper finds that the overarching direction signals a shift toward intelligent farming, but the pace of change will depend on how well the identified barriers are resolved.

Climate stress is reshaping the operational conditions under which agricultural decisions are made. Drought, heat, flooding, salinity, and increasingly variable weather patterns intensify uncertainty around irrigation, fertilisation, pest management, crop selection, and yield planning. Precision agriculture has therefore become a central domain in which artificial intelligence is expected to support adaptive, data-driven decision-making. This systematic review examines how artificial intelligence decision models are used in precision agriculture under climate stress. The review focuses on three connected dimensions: the model families used to support decisions, the data sources on which these models depend, and the sustainability outcomes that are reported. The objective is to clarify whether current evidence supports the claim that artificial intelligence can deliver more resilient and sustainable agricultural management. The review synthesises peer-reviewed journal articles published. The study selection followed a PRISMA-oriented logic, using predefined search strings, inclusion criteria, and screening stages to identify relevant work on machine learning, deep learning, remote sensing, sensor-based agriculture, irrigation optimisation, yield prediction, crop disease detection, and sustainability assessment. No new empirical field data were generated. The evidence base shows rapid expansion in machine learning and deep learning applications, especially for yield prediction, irrigation management, crop monitoring, plant disease detection, and smart farming decision support. Satellite imagery, UAV data, IoT sensors, weather variables, soil information, and farm-management records appear as the dominant data sources. However, the review also identifies uneven attention to climate stress conditions, limited integration of long-term climate projections, and inconsistent reporting of sustainability outcomes.The review concludes that artificial intelligence for precision agriculture under climate stress is technically promising but methodologically fragmented. Most studies emphasise model accuracy, yield improvement, or input optimisation, while fewer evaluate environmental trade-offs, social impacts, farmer livelihoods, or long-term resilience. A more rigorous, transparent, and sustainability-centred research agenda is needed to move the field beyond predictive performance toward climate-resilient agricultural decision systems.

Agricultural artificial intelligence is increasingly shaped by a precision paradigm that treats good farming as the increasingly fine-grained optimization of inputs, outputs, risks, and operational decisions. This paradigm is attractive because it promises measurable efficiency, predictive control, and scalable decision support across complex farming systems. In climate-sensitive agriculture, however, the same orientation can narrow what counts as a good decision. The problem is that climate-sensitive farming is not merely a technical setting in which better data automatically produce better outcomes. Farmers must act under uncertainty, ecological variability, long time horizons, and ethically contested trade-offs involving productivity, resilience, livelihoods, and environmental stewardship. These conditions expose the limits of optimization as a sufficient guide for responsible agricultural AI. This article develops a conceptual framework that shifts the guiding principle of agricultural AI from precision to prudence. Prudence is understood as practical wisdom: the capacity to deliberate well about what is fitting, responsible, and contextually appropriate when action is necessary but certainty is unavailable. The framework therefore reorients AI from prescribing optimal decisions to supporting wise and accountable judgment. The article is based on a conceptual synthesis peer-reviewed publications across agricultural ethics, responsible innovation, precision agriculture, AI governance, climate adaptation, and science and technology studies. It critiques precision agriculture as an optimization paradigm, articulates prudence as a responsible AI principle, and develops a framework for climate-sensitive decision support. Two tables contrast the precision and prudence paradigms and present the proposed conceptual framework. The central argument is not that agricultural AI should abandon data, modelling, prediction, or decision support. Rather, responsible AI for climate-sensitive farming should use these capacities to enlarge human judgment rather than replace it. A prudent AI system helps farmers deliberate under uncertainty, compare trade-offs, question recommendations, and remain accountable for decisions that are ecological, social, and moral as well as technical.