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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.

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.

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.

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.

Digital twins are increasingly proposed as cyber-physical representations that may support precision agriculture through monitoring, simulation, prediction, optimization, and decision support. However, agricultural systems described as digital twins vary substantially in data quality, interoperability, synchronization, modelling depth, predictive reliability, decision functionality, validation, and operational governance. This article proposes a conceptual maturity framework that evaluates agricultural digital twins through capability progression rather than technology adoption alone. The framework begins with descriptive monitoring and advances through real-time farm representation, simulation and scenario analysis, prediction and optimization, and supervised autonomous decision support. Monitoring maturity depends on reliable sensing, remote sensing, Internet of Things integration, data fusion, spatial and temporal adequacy, traceability, and human-interpretable representation. Simulation and prediction maturity require explicit farm-system boundaries, calibrated models, uncertainty estimation, local validation, and evidence that analytical outputs remain valid for their intended context. Optimization maturity additionally requires transparent objectives, constraints, robustness assessment, and distinction between predictive accuracy and decision value. The highest proposed level permits only narrowly bounded and supervised decision initiation supported by human authority, explainability, safety mechanisms, governance, cybersecurity where relevant, and continuous feedback. The framework does not constitute a validated maturity standard or establish agronomic, environmental, economic, or autonomous decision benefits. Its principal contribution is an integrated structure for assessing capability readiness, identifying progression barriers, and preventing monitoring, simulation, prediction, or optimization functions from being overinterpreted as evidence of deployment-ready autonomy.