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Obtaining body mass in large-scale rearing and production environments is difficult, making non-contact assessment methods indispensable. This paper introduces an original pipeline for predicting pig body mass based on an adapted mask region convolutional neural network (mask R-CNN). The adaptation deploys ResNeSt as the backbone feature-extraction architecture, aiming to bolster the capacity to derive features from imagery. A feature pyramid network (FPN) was appended to this backbone to enable feature merging across multiple scales. Within the region proposal network (RPN), a channel attention mechanism (CAM) was installed alongside a spatial attention mechanism (SAM), enabling the dynamic combination of localized characteristics with their long-range interdependencies. This configuration captures holistic contextual knowledge, ultimately improving segmentation fidelity. The adapted architecture achieved precision (P) of 90.33%, recall (R) of 89.85%, and mean average precision (MAP) of 95.21%, thereby isolating pig areas in images with notable effectiveness. Five descriptive features drawn from the imagery were examined: back area, body length, body width, average depth, and eccentricity. Leveraging the pig depth images, five regression learning algorithms—ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)—were trained to forecast mass values. AdaBoost delivered the strongest predictive performance, evidenced by a coefficient of determination (R2) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg², and a mean absolute percentage error (MAPE) of 8.45%. The experimental results confirm that machine learning models can reliably predict pig mass, providing a technical foundation for the informed management of pig production facilities.

The tractor functions as a fundamental power source for farming activities. Still, the available power is frequently not fully exploited because of poor matching between the tractor and the attached implement under actual field conditions. To improve overall system productivity, it is essential to gather information on performance-related variables characterising the tractor-implement unit. In this work, we build a real-time Instrumented Tractor Performance Monitoring System (ITPMS) that leverages the Internet of Things (IoT). The hardware foundation comprises a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer records tillage depth, whereas the three-point hitch dynamometer captures draft force values. Proximity sensors are mounted on a two-wheel-drive (2WD) tractor to measure forward speed and drive-wheel slip. A dedicated web server is set up within a Google® Firebase® project, allowing storage of data streams from all sensors via the Raspberry Pi. Alongside this, a web interface and a mobile application are constructed to present the sensor-generated data in real time. Field experiments were conducted to assess and observe the performance variables of the tractor-implement unit employing the built ITPMS. The findings reveal that the system can reliably monitor the performance variables required for the tractor-implement unit. Furthermore, the system’s ability to transmit updated information to the IoT server without delay is verified. Altogether, designing and deploying this Raspberry Pi–centred IoT framework provides a trustworthy and effective approach for real-time performance monitoring of instrumented tractors.

The surveillance of poultry movement patterns is an essential diagnostic tool for welfare evaluation, yet manual monitoring routines impose considerable temporal demands and physical strain, particularly in high-density flocks. Inadequately trained personnel may additionally fail to recognize subtle shifts in behavioral intensity, thereby overlooking critical windows for prophylactic intervention and remedial action. The present work merges conventional camera-based observation with a sophisticated automated recognition architecture to classify diverse broiler conduct within commercial housing. Deployment of a two-stage deep learning pipeline occurs as follows: initial detection localizes individual birds, followed by identification of five principal anatomical landmarks (cranial region, ventral region, bilateral limbs, and caudal appendage). From these points, a skeletal framework anchored in the ventral region is generated, yielding six angular measurements via trigonometric computations. A long short-term memory network subsequently processes these angular sequences, estimating the likelihood across behavioral categories encompassing “Standing”, “Walking”, “Resting”, “Eating”, “Preening”, and “Flapping”, with the highest-probability classification retained. To quantify the temporal distribution between stationary and dynamic states, a cascaded fuzzy-logic inference architecture was implemented, providing a resilient assessment of overall activity magnitude. Validation conducted within a mixed-gender rearing facility demonstrated that the proposed methodology attained performance floors of 85.2% for broiler categorization, 79.2% for anatomical landmark localization, and 50.8% for behavioral classification accuracy. Activity-level determinations generated by the system were consistent with evaluations by veteran poultry husbandry specialists.

Achieving optimal cannabis crop performance and product quality hinges on dependable, automated systems for leaf disease identification that allow for swift diagnosis and action. Present-day automated methods fall short of being purpose-built for the distinct difficulties inherent in spotting cannabis diseases, rarely holding up across shifting environmental contexts and varying phases of plant maturity. This paper proposes a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), grounded in an extensive data collection that mirrors the breadth of cannabis leaf pathologies and their unfolding. The design pairs non-population-based decision merging within the image preparation pipeline with population-based decision merging at the classification stage, calling upon a variety of CNN backbones. The outcome is a meaningful leap in key performance measures: HyAMIS-DLE achieves 99.58% accuracy, besting standard architectures by up to 4.16%, while also offering markedly improved resilience and a boosted Area Under the Curve (AUC) metric, enabling a clear distinction between unaffected and afflicted foliage. Rolling HyAMIS-DLE into our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) confirms its on-the-ground value, helping achieve higher harvest volumes, reduce waste, and advance ecologically sound farming practices.

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.

Intelligent farming systems increasingly depend on data generated through sensors, machinery, satellites, digital platforms, advisory tools, farm management software, and artificial intelligence-enabled decision-support systems. These systems promise more precise monitoring, improved resource allocation, and better responsiveness to environmental and operational uncertainty. As farm operations become more data-intensive, farm data shift from being a by-product of agricultural activity to becoming a strategic asset within intelligent farming systems. This transformation creates governance problems that cannot be resolved through technical data management alone. Farm data raise contested questions about ownership, consent, access, interoperability, platform dependence, accountability, and farmer control. These questions are especially important because data generated on farms may be used beyond the original farming context for commercial analytics, platform development, benchmarking, insurance, input recommendation, credit scoring, or supply-chain governance. The objective of this article is to develop a data stewardship framework for intelligent farms. The framework is designed to clarify how ownership, consent, access, interoperability, accountability, and farmer-control mechanisms can be organised in ways that support responsible digital agricultural innovation. It treats data stewardship as a governance function that mediates between innovation, commercial data use, and the protection of farmers’ decision-making authority. The article is based on a governance framework synthesis  peer-reviewed articles published. It does not present new empirical data. Instead, it synthesises literature on digital agriculture, smart farming, agricultural data governance, platformisation, responsible innovation, interoperability, and farmer data rights to construct a practical and conceptual governance framework. The article maps farm data as a governance problem, analyses ownership and consent issues, examines interoperability and platform dependence, and proposes a stewardship framework for intelligent farms. Three tables summarise ownership and control issues, interoperability risks, and the proposed framework. Responsible intelligent farming requires data stewardship arrangements that protect farmer control while enabling trustworthy data sharing and innovation.

Autonomous farming systems are moving rapidly from experimental prototypes into commercial and operational agricultural settings. Autonomous tractors, robotic weeders, drones, harvest-assist systems, and sensor-guided machines promise to address labour shortages, improve operational efficiency, and support more precise field management. Yet the governance arrangements needed to ensure that these systems remain safe, understandable, and accountable have not developed at the same pace. The central problem is not only whether autonomous machines can perform agricultural tasks accurately. It is whether farmers, workers, contractors, and rural communities can understand, supervise, question, override, and hold these systems accountable when they act in complex farm environments. Without deliberate attention to trust calibration, human override, explainability, and accountability, autonomous farming may produce technically impressive but socially fragile systems. This article develops a Human-Centred Governance Framework for autonomous farming. The framework is designed around four pillars: calibrated trust, meaningful human override, explainable decision-making, and clear accountability structures. Its purpose is to shift governance attention from machine capability alone toward the conditions under which autonomous systems can be responsibly integrated into agricultural work. The proposed framework shows that human-centred governance is not an optional ethical supplement to autonomous farming. It is a practical requirement for safe deployment, legitimate adoption, and accountable operation. By integrating trust, override, explainability, and accountability, the framework provides guidance for farm managers, technology developers, regulators, and agricultural service providers.

Artificial intelligence is increasingly promoted as a tool for improving agricultural productivity, food-system coordination, sustainability monitoring, risk prediction, quality control, and decision support. Across agri-food systems, AI is expected to support more timely decisions, more efficient resource use, and more adaptive responses to climatic, market, and production uncertainty. These expectations have encouraged investment in digital tools, analytics platforms, automated monitoring systems, and predictive applications. Yet the availability of AI tools does not mean that agri-food systems are prepared to use them effectively or responsibly. Readiness depends on the quality of data infrastructure, the strength of institutions, the capabilities of farmers and other users, and the maturity of governance arrangements. When these conditions are weak, AI implementation can deepen inequality, create dependence on opaque platforms, reduce trust, or generate risks that are difficult to identify and manage. This article develops an agri-food AI readiness framework that explains the conditions required for responsible and effective AI implementation. The framework conceptualises readiness as a socio-technical condition produced through the interaction of data infrastructure and digital capacity, institutional capacity and policy support, farmer capability and adoption readiness, and governance maturity and risk management. It emphasises that AI readiness is not a single technical threshold but a combined maturity condition across interconnected system dimensions. The framework contributes a structured way to assess whether agri-food systems are prepared to adopt, adapt, govern, and benefit from AI. It identifies how weaknesses in one dimension can reduce the effectiveness of the others, even where advanced technologies are available. By linking implementation capacity with governance responsibility, the framework supports more inclusive, trustworthy, and context-sensitive pathways for agri-food AI.

Artificial intelligence is increasingly embedded in agricultural decision support, farm platforms, automation, sensing, advisory systems, and data-driven management. These systems influence how farms interpret risk, allocate inputs, monitor environments, plan production, and respond to market and ecological uncertainty. As agricultural AI becomes more consequential, its governance must address not only technical performance but also the social conditions under which digital decisions are made, trusted, challenged, and revised. Agricultural AI governance is often framed around technical accuracy, transparency, regulatory compliance, or institutional oversight. While these are necessary, they are insufficient when farmers have limited influence over system design, data practices, model assumptions, recommendation logic, or dispute pathways. Governance weaknesses can produce exclusion, loss of autonomy, data misuse, algorithmic opacity, dependency on platforms, and digital decisions that affected users cannot meaningfully challenge. This article develops a participatory AI governance framework for agriculture centred on farmer voice, local knowledge integration, contestable digital decisions, and accountability safeguards. The framework treats governance as a socio-technical practice that connects design, deployment, monitoring, revision, and redress. It argues that responsible agricultural AI requires institutional mechanisms through which farmers and other affected actors can shape, interpret, evaluate, contest, and improve digital systems. The framework contributes to agricultural AI governance by showing how participation can strengthen legitimacy, trust, accountability, and contextual fit. It positions farmer voice as a condition of decision legitimacy, local knowledge as a condition of contextual accuracy, contestability as a condition of procedural fairness, and accountability safeguards as a condition of responsible institutional performance. Participatory governance is therefore presented as a practical foundation for agricultural AI that is more locally grounded, explainable, responsive, and accountable.

Agri-food systems increasingly rely on analytics, artificial intelligence, machine learning, environmental monitoring, and decision-support tools to evaluate sustainability performance. These systems can estimate resource use, model environmental impacts, detect risks, and inform actions across farms, supply chains, markets, and governance settings. As analytics become embedded in sustainability decisions, their outputs increasingly shape how environmental problems are recognised and acted upon. Yet sustainability analytics does not become useful or responsible simply because it produces indicators, predictions, rankings, or dashboards. Environmental metrics may be technically sophisticated while remaining difficult for farmers, firms, auditors, regulators, and communities to interpret or challenge. This creates an explainability problem in which users need to understand not only what an analytic system reports, but also how the result was produced, what assumptions it contains, and what consequences may follow from acting on it. This article develops a conceptual framework for explainable sustainability analytics in agri-food systems. The framework treats explainability as a system-level requirement for linking environmental impact interpretation, risk explanation, responsibility attribution, human review, and accountability safeguards. It argues that sustainability analytics should be evaluated not only by predictive performance or measurement coverage, but also by whether it supports transparent, contestable, and context-sensitive decision-making. The article contributes a structured framework for interpreting environmental impact, explaining risk, attributing responsibility, and supporting accountable review in agri-food decision contexts. It positions explainable sustainability analytics as an approach that connects data, models, indicators, explanation mechanisms, human judgement, and governance safeguards. In doing so, it provides a foundation for sustainability analytics that is more interpretable, trustworthy, and actionable.

Agri-food systems increasingly rely on artificial intelligence for farm decision support, automation, quality control, sustainability monitoring, logistics, supply-chain coordination, risk prediction, and resource management. These applications promise productivity gains and improved responsiveness to environmental and market pressures. Yet their growing influence also creates new governance demands because AI systems can shape decisions that affect farms, firms, workers, consumers, ecosystems, and public institutions. Trustworthy AI in agri-food systems cannot be established through technical performance claims alone. A model may be accurate in a narrow test environment while remaining opaque, unsafe under changing field conditions, unfair across producer groups, or unable to substantiate sustainability claims. Certification therefore needs to move beyond reputational signalling and provide credible assurance that AI systems can be examined, challenged, monitored, and held accountable. This article develops a trustworthy AI certification framework for agri-food systems centred on auditability, safety, fairness, and sustainability-claim verification. The framework treats certification as a governance mechanism rather than a simple label or checklist. It identifies the evidence, assurance procedures, monitoring expectations, and institutional safeguards needed to make certification credible in technically complex and socially consequential agri-food settings. The article contributes a structured approach for regulators, certification bodies, technology providers, agri-food firms, farmer organisations, sustainability auditors, and public institutions. It argues that trustworthy AI certification should depend on verifiable evidence, independent auditability, lifecycle risk assurance, distributional accountability, environmental claim verification, and clear responsibilities across the AI value chain. Certification becomes meaningful only when it supports systems that are auditable, safe, fair, environmentally credible, and publicly accountable.

Intelligent agriculture increasingly uses artificial intelligence, sensors, remote sensing, automation, modelling, and decision-support systems to monitor and manage agricultural production. These tools can strengthen observation, prediction, and coordination across farms and landscapes. Yet their contribution to agricultural transformation depends on the ecological aims toward which they are designed and governed. Regenerative agriculture requires more than improved efficiency or narrower optimisation of inputs. It calls for soil restoration, biodiversity protection, ecological feedback, and long-term system health. Intelligent systems therefore need to be evaluated not only by productivity gains, but also by their capacity to support ecological recovery. This article develops a conceptual framework for regenerative intelligence in agriculture. Regenerative intelligence is defined as the AI-supported capacity of agricultural systems to monitor, interpret, and support soil restoration, biodiversity protection, and long-term system health through adaptive and ecologically accountable decision pathways. The framework explains how sensing, ecological analytics, modelling, decision support, monitoring, feedback, and governance can contribute to regenerative agricultural transformation. It also clarifies that AI-supported agriculture is not automatically regenerative. Its regenerative value depends on ecological indicators, farmer participation, responsible governance, biodiversity sensitivity, soil-health interpretation, and sustained learning from ecological outcomes.