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This investigation puts forward a novel intelligent farming platform that merges Internet of Things (IoT) capabilities with predictive modeling and self-regulating command systems to streamline watering processes and amplify resource productivity. The suggested design employs soil wetness, thermal, and atmospheric moisture probes interfaced with an Arduino-centered microcontroller unit, allowing irrigation to be triggered autonomously according to live sensor readings. Agricultural operators can remotely oversee and adjust watering cycles through portable devices, delivering both exactness and operational simplicity. The predictive model fuses historical data with current measurements to anticipate future irrigation demands, guaranteeing moisture delivery exclusively when truly required. Empirical on-farm assessments indicated a 30 % decline in water expenditure versus standard irrigation techniques, whilst upholding ideal earth moisture parameters. In addition, the framework markedly diminished workforce and power expenses, bolstering overall functional capability. By confronting the issue of hydrological shortage and advancing conscientious resource stewardship, this intelligent platform possesses the capability to enhance harvest volumes, curtail greenhouse gas discharges, and further eco-conscious cultivation methodologies. Within water-limited geographies, deploying this technological solution constitutes a pivotal stride towards enduring agricultural viability, indicating considerable scope for extensive implementation.

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.

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.

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.

Artificial intelligence is increasingly positioned as a transformative force for climate-smart agriculture, promising simultaneous gains in productivity, efficiency, and sustainability. Across agricultural policy, innovation systems, and digital farming markets, AI is presented as a solution to climate uncertainty, resource scarcity, labour pressures, and food-security demands. This review examines that promise through a critical lens. The dominant narrative surrounding AI-enabled climate-smart agriculture often treats optimization as a neutral technical process. Yet algorithmic systems do not merely improve farming decisions; they also define what counts as efficiency, which outcomes are prioritised, and whose knowledge becomes authoritative. This raises important questions about power, agency, and sustainability. The objective of this critical review is to interrogate the politics of AI in climate-smart agriculture. It synthesises evidence on the tensions between productivity, efficiency, sustainability, farmer agency, data sovereignty, and governance. The review asks how algorithmic optimization reshapes agricultural futures and who benefits from this transformation. The review draws on  peer-reviewed articles published across agricultural innovation, sustainability science, digital agriculture, artificial intelligence, and critical data studies. Rather than reporting new empirical data, it develops a critical synthesis of existing debates and identifies conceptual gaps in current scholarship. The review finds that algorithmic farming is not simply a technical evolution of precision agriculture. It is a contested political terrain in which sustainability goals, commercial interests, data infrastructures, and farmer autonomy are negotiated. A critical research agenda is needed to ensure that AI contributes to equitable, democratic, and ecologically resilient agricultural futures.

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.

Digital twin technology has moved from industrial cyber-physical systems into agriculture as part of the wider shift toward intelligent, data-intensive farming. In farming contexts, digital twins are expected to connect physical production systems with virtual models that can monitor, simulate, and support decisions in near real time. Yet the agricultural literature remains fragmented across crop, soil, water, livestock, sensor, modelling, and innovation studies. This scoping review maps the emerging evidence on digital twins in intelligent farming systems. Its objective is to classify the applications, architectures, and translation barriers reported in peer-reviewed research. The review focuses on how digital twin concepts are adapted to agriculture rather than treating the term as a generic label for digital monitoring. The review follows a scoping logic to chart the breadth of the field rather than evaluate intervention effectiveness. A total of 29 peer-reviewed articles were used to map conceptual definitions, technical architectures, agricultural applications, and implementation constraints. The evidence was organised around three guiding questions: what architectures are proposed, what agricultural domains are covered, and what barriers restrict translation. The mapped literature shows that agricultural digital twins are most often framed around IoT-enabled sensing, cloud or edge data processing, simulation models, decision dashboards, and feedback loops. Applications are concentrated in crop management, greenhouse production, irrigation, autonomous machinery, and livestock monitoring, while soil-system and whole-farm twins remain less mature. The literature also reveals persistent barriers related to data interoperability, connectivity, cost, skills, governance, standards, and uncertain return on investment. The review concludes that digital twins hold significant promise for intelligent farming but remain concentrated at the prototype and demonstration stages. Moving from research concepts to practical farm systems requires coordinated progress in data infrastructure, modular architectures, validation studies, standards, and farmer-centred implementation models. The review provides a structured map for future digital agriculture research and practice.

Intelligent agriculture is often presented as the next great leap in farming: a data-rich, sensor-driven, algorithmically guided transformation of how food is produced. By combining artificial intelligence, Internet of Things infrastructures, remote sensing, robotics, and big data analytics, it promises to make farming more precise, efficient, and responsive. Yet much of this promise remains tied to an older and narrower ambition: producing more output per hectare. This perspective argues that intelligent agriculture has inherited and digitally extended the yield-maximization logic of twentieth-century productivist agriculture. The problem is not that yield is unimportant, but that yield has become too dominant as the organizing measure of agricultural progress. When farming systems are judged primarily by short-term productivity, the underlying ecological foundations of production can be degraded while the system still appears technically successful. The article advances a reframing of intelligent agriculture around three interconnected goals: soil health, biodiversity, and long-term farm resilience. These are not secondary environmental concerns to be considered after productivity has been secured. They are the living infrastructure of agriculture and the conditions through which future productivity, livelihoods, and food security become possible. The central claim is that the future of intelligent agriculture should not be defined by producing more at any cost. It should be defined by producing with care: sustaining soil function, enhancing biodiversity, and strengthening the adaptive capacity of farms under climate uncertainty. This requires not simply better technologies, but a deliberate change in the goals, metrics, and values that guide agricultural innovation.

Artificial intelligence is increasingly presented as a transformative force in agriculture, promising more precise decisions, better resource use, improved productivity, and more resilient farming systems. Yet much of this promise has been shaped around large-scale, capital-intensive, data-rich farms in high-income settings. This creates a structural mismatch between dominant agricultural AI imaginaries and the everyday realities of smallholder farming. Smallholder farmers operate under conditions that are often treated as barriers to technological adoption rather than as core design conditions. Limited connectivity, intermittent electricity, restricted access to smartphones, low disposable income, linguistic diversity, and uneven access to advisory support all shape how digital tools are encountered and used. When these realities are ignored, agricultural AI risks widening rather than narrowing existing inequalities. This article develops a Smallholder-Centred AI Model for agriculture. The model reorients agricultural AI around four foundational principles: low-resource design, inclusion and affordability, context-awareness, and farmer participation. It argues that these principles must not be added after technical development but must structure the design, governance, and evaluation of AI from the beginning. The article concludes that AI can serve smallholder agriculture only when smallholders are positioned as active participants in the intelligence of the system. A smallholder-centred approach requires technologies that work with limited infrastructure, respond to local agroecological and social contexts, respect farmer knowledge, and remain affordable and accountable. The future of agricultural AI must therefore be judged not only by predictive accuracy but by its capacity to support inclusive, locally grounded, and farmer-led transformation.

Artificial intelligence is increasingly used across agriculture and food systems for precision farming, yield prediction, pest detection, supply-chain optimisation, quality control, decision support, and resource management. These applications are often promoted as tools for increasing productivity, reducing uncertainty, improving resource efficiency, and strengthening food-system resilience. As AI becomes embedded in farms, supply chains, laboratories, and food governance infrastructures, its consequences extend beyond technical performance. The central problem is that the benefits of agri-food AI are frequently assessed through accuracy, automation efficiency, profitability, or optimisation metrics, while broader ethical, equity, environmental, and governance implications receive less systematic attention. AI systems may alter farmer autonomy, labour relations, data ownership, market participation, environmental decision-making, and institutional accountability. Treating these consequences as secondary risks understates the degree to which AI changes relationships among people, technologies, land, food, and power. This critical review examines responsible AI in agriculture and food systems through four connected dimensions: ethics, equity, sustainability, and governance. It argues that responsible AI cannot be reduced to technical reliability or model performance because agri-food systems are socially embedded, ecologically consequential, and institutionally fragmented. The article therefore evaluates responsible AI as a socio-technical and governance challenge rather than as a purely computational problem. The article contributes a critical argument that responsible agri-food AI requires accountable design, inclusive access, sustainability-aware evaluation, and multi-level governance. It emphasises that trustworthy AI in agriculture and food systems depends on who controls data, who benefits from automation, who bears environmental and social risks, and who participates in decision-making. Responsible AI is therefore positioned as a core condition for equitable, sustainable, and publicly legitimate agri-food transformation.

Foundation models and multimodal artificial intelligence are beginning to reshape agricultural AI by enabling systems that can integrate images, text, sensor streams, remote-sensing data, environmental records, and management information. These approaches differ from earlier narrow models because they can reuse learned representations across related tasks and support richer forms of agricultural interpretation. Their potential relevance spans crop monitoring, disease detection, advisory services, automation, forecasting, and agri-food decision support. Despite this promise, the transfer of large-scale AI systems into agriculture remains complex. Agricultural environments are biologically variable, spatially heterogeneous, seasonally dynamic, and shaped by management decisions that are often poorly captured in available datasets. As a result, questions remain about data quality, annotation adequacy, domain adaptation, validation, explainability, governance, and deployment risk. This scoping review maps emerging applications, data requirements, and deployment risks associated with foundation models and multimodal AI in agriculture and agri-food systems. It focuses on how the reviewed literature frames model architectures, data modalities, application domains, validation needs, and governance concerns. The purpose is not to estimate pooled effectiveness but to chart the evidence landscape and clarify where knowledge is advancing or remains unsettled. The review uses a scoping approach to organise literature around key concepts, model types, data inputs, agricultural tasks, implementation settings, and responsible deployment concerns. It charts relationships among remote-sensing foundation models, vision-language systems, large language models, generative AI, multimodal data fusion, benchmark datasets, and agricultural decision-support systems. It also identifies gaps in field validation, interoperability, low-resource contexts, participatory governance, and trustworthy deployment. The review contributes a structured synthesis of how foundation models and multimodal AI may extend agricultural sensing, prediction, automation, and decision support. It highlights that technical capability alone is insufficient for responsible agricultural use. Robust data infrastructures, context-aware adaptation, transparent validation, and accountable governance will be central to whether these systems support sustainable and equitable agri-food transformation.

Digital agriculture technologies are increasingly positioned as tools for strengthening food security through improved sensing, prediction, automation, decision support, and resource management. These technologies are promoted as ways to help agricultural systems respond to climate stress, resource constraints, production volatility, and disruptions across agri-food value chains. Their potential relevance extends from farm-level monitoring to broader food-system coordination. Evidence on digital agriculture is distributed across multiple technology domains, including artificial intelligence, Internet of Things systems, robotics, remote sensing, and decision-support platforms. Food-security implications are often reported indirectly through productivity, efficiency, sustainability, resilience, risk reduction, or decision-quality outcomes. This dispersion makes it difficult to judge where the evidence is strongest and where food-security claims remain underdeveloped. This umbrella review synthesises review-level evidence on digital agriculture technologies in relation to food-security pathways. It examines how the reviewed literature connects digital agriculture with food availability, access, utilisation, stability, resilience, sustainability, productivity, and agri-food decision-making. The article focuses on review evidence rather than primary empirical findings. The approach compares review-level evidence across technology domains, outcome pathways, implementation conditions, and evidence gaps. It gives particular attention to whether digital agriculture evidence demonstrates direct food-security relevance or supports more indirect pathways through resource efficiency, monitoring capacity, operational timeliness, or improved decision support. It also distinguishes technical potential from demonstrated system-level benefit. The contribution of this umbrella review is to clarify where digital agriculture evidence is most developed, where food-security links remain indirect, and what future research is needed to evaluate digital agriculture as a contributor to resilient and inclusive food systems. The synthesis shows that digital agriculture should be assessed not only by technical performance but also by its capacity to support equitable, sustainable, and context-sensitive food-security outcomes.

Intelligent agriculture is increasingly shaped by automation, data platforms, decision-support tools, robotics, sensors, and algorithmic advisory systems. These technologies are promoted as ways to improve productivity, precision, efficiency, and sustainability in farming. Yet they also alter how agricultural decisions are made, who participates in those decisions, and whose knowledge is treated as authoritative. The central problem addressed in this article is that intelligent agriculture is often presented as a technical improvement while its implications for farmer agency remain contested. Farmer autonomy, judgement, dependency, skill, and voice can be strengthened by digital tools, but they can also be narrowed when systems become opaque, prescriptive, proprietary, or difficult to contest. This tension makes farmer agency a core question for responsible intelligent agriculture. This critical interpretive review examines how automation, platformization, and participatory design reshape farmer agency. It interprets agency not as an individual trait that farmers either possess or lose, but as a socio-technical capacity formed through relations among farmers, machines, platforms, data infrastructures, advisors, markets, and governance arrangements. The review therefore focuses on how agency is enabled, constrained, redistributed, and contested. The article examines key concepts, tensions, and assumptions in the reviewed literature. It does not report new primary empirical data, but critically synthesises existing scholarship to clarify how intelligent agriculture reorganises decision-making, knowledge, dependency, participation, power, and accountability. Particular attention is given to tensions between efficiency and autonomy, automation and situated judgement, platform convenience and dependency, data-driven advice and local knowledge, and participation and tokenism. The review argues that farmer agency is redistributed through socio-technical arrangements rather than simply preserved or removed by technology adoption. Responsible intelligent agriculture requires accountable automation, fair platform governance, participatory design, recognition of local knowledge, and institutional mechanisms that give farmers meaningful voice. Farmer agency should therefore be treated as a condition of technological legitimacy, not as an afterthought to innovation.

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.

Smart agriculture increasingly combines artificial intelligence, sensor networks, automation, remote sensing, robotics, and decision-support systems to guide agricultural production and resource management. These technologies promise more precise monitoring, faster diagnosis, improved input targeting, and better coordination of farm operations. Their sustainability value, however, depends on how optimisation goals are defined and how decisions interact with ecological, technical, and social systems. A central problem is that algorithmic systems are often evaluated through yield gains, input efficiency, prediction accuracy, or operational performance. These metrics are important, but they do not necessarily reveal whether an improvement in one sustainability domain shifts pressure onto another. A farm system may become more productive while increasing water demand, energy dependence, biodiversity risk, or unequal exposure to technological burdens. This article develops a conceptual model of algorithmic sustainability trade-offs in smart agriculture. The model explains how yield, water, energy, biodiversity, and equity operate as connected outcome domains rather than isolated evaluation categories. It treats trade-offs as system-level tensions that emerge through optimisation objectives, data availability, decision rules, platform design, governance arrangements, and implementation contexts. The article contributes a systems-oriented framework for analysing when smart agriculture supports sustainability and when it redistributes environmental or social costs. It argues that algorithmic optimisation should not be treated as automatically sustainable simply because it improves efficiency or prediction. Instead, smart agriculture should be assessed by how its decisions balance productivity, resource use, ecological resilience, and distributional fairness.

Intelligent agriculture increasingly incorporates automation, artificial intelligence, robotics, sensors, digital farm platforms, remote sensing, and predictive analytics into everyday farm management. These systems are frequently presented as tools for improving efficiency, precision, productivity, and labour allocation. Yet their value cannot be assessed only through technical performance or economic output. Their deeper significance lies in how they reshape the relationship between farmers, knowledge, technology, and agronomic decision-making. A central risk is that automation may substitute for farmer expertise rather than support it. When digital systems hide decision logic, prescribe action without explanation, or reduce farmers to passive operators, they can weaken practical skill, situated judgment, and opportunities for learning. Such effects are especially important in agriculture, where decisions are made under ecological variability, uncertainty, and local specificity. Intelligent systems therefore require evaluation not only as tools of control, but also as environments that can either preserve or erode human capability. This article develops a human-centred conceptual model for shifting from automation-oriented replacement toward augmentation-oriented support in agriculture. The model is organised around three connected capability domains: skill preservation, farmer judgment, and agronomic learning. It argues that intelligent agricultural systems become augmentative when they extend perception, improve interpretation, support deliberation, enable feedback, and preserve meaningful farmer control. The model also identifies design principles and boundary conditions that determine whether digital systems strengthen or weaken human capability. The article contributes a structured way of assessing intelligent farming beyond labour substitution, prediction accuracy, or platform efficiency. It proposes that digital agricultural systems should be judged by whether they help farmers notice more, reason better, decide more reflectively, and learn more effectively from farm outcomes. This framing positions augmentation as a human-centred design and governance challenge, not merely a technical feature. Agricultural intelligence is therefore understood as a shared capability between people, technologies, advisory systems, and situated agronomic practice.

Intelligent agriculture increasingly relies on data-intensive systems, including sensors, connectivity, decision-support tools, automation, platforms, machine learning, and analytics. These systems are often presented as pathways toward more productive, efficient, resilient, and sustainable farming. Yet their social effects depend on how access, control, capability, risk, and value are distributed across agricultural actors. The central problem addressed in this article is that intelligent agriculture can deepen inequality when digital systems are unevenly organised. Farmers and rural actors may face different levels of access to infrastructure, data, technical support, platform services, financial capacity, and bargaining power. As a result, the benefits of digitalisation may accumulate among actors already positioned to use, govern, and monetise data-driven systems. This article develops a theory of digital inequality in intelligent agriculture. The theory explains how data access, infrastructure gaps, platform dependence, and uneven value capture interact to shape distributional outcomes. It treats inequality not as a side effect of incomplete adoption but as a process produced through socio-technical relations. The approach integrates scholarship on digital agriculture, rural digital divides, agricultural platforms, farmer capability, data governance, infrastructure inequality, responsible innovation, and value distribution. Rather than treating smart farming as a neutral technological transition, the article develops constructs, mechanisms, relationships, boundary conditions, and propositions. The aim is to explain how digital agricultural systems can reproduce, intensify, or reduce inequality.The proposed theory argues that digital inequality in intelligent agriculture emerges through linked pathways of data exclusion, infrastructure disadvantage, capability constraint, platform control, and value appropriation. These pathways influence who can participate in intelligent agriculture, who becomes dependent on digital intermediaries, who bears risks, and who captures benefits. The article concludes that intelligent agriculture should be evaluated by its distributional consequences as well as its technical performance.

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.

Intelligent agriculture increasingly relies on sensors, remote sensing, machine learning, automation, digital platforms, and decision-support systems to produce rapid agricultural insight. These systems promise earlier detection, faster response, and more precise management across crop, soil, water, livestock, and farm-business decisions. Their appeal is closely tied to the speed, volume, and apparent objectivity of agricultural data streams. Yet agricultural intelligence cannot be reduced to fast data alone. Farms are living, seasonal, historically shaped, and locally interpreted systems in which the meaning of data depends on field context, management history, ecological feedback, and farmer judgement. Rapid signals can be useful, but they can also encourage premature conclusions when detached from temporal and local conditions. This perspective argues that intelligent agriculture needs slow data to remain contextually valid, seasonally sensitive, locally meaningful, and responsibly updated. Slow data is defined here as context-rich, temporally grounded, locally interpreted, ecologically informed, and responsibly revised data that strengthens agricultural AI by preventing overfitting, decontextualised automation, and unexamined model drift. It complements rather than rejects fast sensing and predictive analytics. The article develops a slow data perspective for intelligent agriculture by connecting context, seasonality, local knowledge, farmer feedback, model revision, and governance safeguards. It argues that agricultural AI becomes more responsible when it slows down interpretation enough to recognise uncertainty, ecological variability, and the limits of model transfer. The contribution is a perspective on agricultural intelligence that is faster when needed, but patient enough to remain agronomically meaningful.