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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.

Remote sensing and machine learning have become prominent components of smart farming research, generating an expanding body of review literature across agricultural monitoring and prediction applications. This umbrella review synthesises review-level evidence concerning crop monitoring, disease detection, yield forecasting, and decision support while examining the methodological conditions that shape evidence strength and transferability. Peer-reviewed systematic reviews, scoping reviews, structured reviews, surveys, meta-reviews, and other eligible review-level articles were identified through searches of major academic databases and publisher platforms. Evidence was organised by application domain, sensing modality, analytical method, validation design, generalisation, uncertainty, reproducibility, and decision-support relevance. Crop-monitoring reviews describe extensive use of satellite, unmanned aerial vehicle, multispectral, hyperspectral, thermal, and multimodal data for crop mapping, growth assessment, phenotyping, stress monitoring, and related applications. Disease-detection reviews report rapid expansion of image-based machine learning and deep learning, but repeatedly identify data scarcity, class imbalance, curated-image dependence, and limited cross-context validation. Yield-forecasting reviews identify recurring integration of remote sensing, weather, soil, crop-model, and management information, although forecast horizons, spatial and temporal validation, uncertainty communication, and decision relevance remain inconsistent. Across domains, reported technical promise is moderated by heterogeneous datasets, benchmark inconsistency, review overlap, incomplete reproducibility, and limited evidence of transfer across crops, regions, seasons, and acquisition conditions. Review-level evidence supports a substantial research opportunity for remote sensing and machine learning in smart farming, but it does not independently establish field readiness, diagnostic reliability, operational forecasting performance, or deployment readiness. Stronger review reporting, external validation, transparent benchmarking, uncertainty assessment, reproducible workflows, and responsible decision-support evaluation are required.

Regenerative agriculture is increasingly associated with soil improvement, ecological restoration, climate adaptation, and more resilient farming systems, yet these outcomes cannot be inferred from a management label or from digital measurement alone. This article develops a conceptual framework for data-driven regenerative agriculture that links precision farming, soil health monitoring, biodiversity monitoring, ecological feedback, climate-risk interpretation, farmer participation, and responsible decision support. The framework approach treats regeneration as a context-dependent monitoring and interpretation problem involving heterogeneous data, multiple spatial and temporal scales, uncertain ecological relationships, and explicit claim boundaries. Precision farming contributes spatially and temporally resolved observations from sensors, remote sensing, soil sampling, crop monitoring, water data, and farm-management records, while soil-health interpretation requires function-linked indicators, longitudinal baselines, local validation, and separation of monitoring from verified restoration or carbon-accounting claims. Biodiversity monitoring is organized around agrobiodiversity, functional biodiversity, habitat conditions, landscape context, ecological indicators, and field-validated observation methods. Climate resilience is framed through hazards, exposure, vulnerability, adaptive capacity, farm-system adaptation, and uncertainty rather than as a guaranteed outcome. Regenerative decision support integrates these domains through data-quality review, provenance, interoperability, ecological interpretation, farmer knowledge, human oversight, sustainability feedback, and iterative framework updating. The principal contribution is a structured conceptual architecture for connecting digital agricultural capabilities with cautious ecological reasoning. The framework supports research design and monitoring-system development but does not independently verify regenerative outcomes, environmental performance, biodiversity recovery, soil restoration, carbon sequestration, climate resilience, operational safety, or deployment readiness.