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Rice blast (RB) and brown spot (BS) represent two economically impactful diseases in rice cultivation, responsible for substantial annual yield reductions. Both pathogens infect the same host plant and produce lesions of remarkably similar appearance, leading to frequent misidentification by growers. Correct identification is vital to ensure effective disease control. Visual diagnosis requires trained personnel, while laboratory-based molecular assays, although precise, are costly and time-consuming. The present study examined whether lesions from these two diseases could be distinguished using proximally sensed digital RGB images and calculated colour indices. A smartphone camera was used to capture digital photographs of the lesions. A set of thirty-six colour indices was assessed via k-means clustering for disease separation, utilising three colour channel systems: RGB, HSV, and Lab. In summary, image backgrounds were masked so that only the leaf spot lesion remained, and colour indices were then computed as features from the lesion’s central portion, reflecting the diagnostic practice commonly employed by plant pathologists. The outcomes indicated that the complete set of 36 indices separated the two diseases with an accuracy of 84.3%. It was also noted, however, that accuracy was largely driven by indices related to the R, G, and B profiles, with the remaining indices contributing little. The indices G/R, NGRDI, (R + G + B)/R, VARI, (G + B)/R, R/G, Nor_r, G-R, Mean_A, and Logsig were singled out as providing the greatest contribution to disease discrimination. Accordingly, these specific RGB-derived colour indices can be applied alongside the k-means algorithm to separate blast and brown spot diseases. The study’s outcomes deliver a non-destructive, alternative objective means of recognising RB and BS disease symptoms. On the strength of these results, a mobile application named Blast O spot was created to perform field-level disease differentiation.

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.

Smart farming is increasingly promoted as a pathway for improving agricultural productivity, resource efficiency, climate responsiveness, and data-driven decision-making. Its core technologies include sensors, digital platforms, machine learning, remote sensing, robotics, automation, and decision-support systems. These tools are often presented as mechanisms for making agriculture more precise, predictable, and adaptive. However, smart farming is not only a technical shift in agricultural tools. It also changes how knowledge is produced, how decisions are justified, how risks are distributed, and how farmers interact with platforms, advisers, suppliers, regulators, and markets. A critical review perspective is therefore needed to examine the social, institutional, political, and ecological consequences of agricultural digitalization. This article critically reviews smart farming as a socio-technical transformation rather than a purely technical modernization process. It examines how datafication, digital governance, farmer adoption, platform dependency, power asymmetry, and sustainability tensions shape the meaning and consequences of smart farming. The central argument is that smart farming should be evaluated through its effects on data control, farmer agency, institutional accountability, and ecological responsibility. The review synthesizes scholarship on digital agriculture, precision agriculture, smart farming, data governance, adoption barriers, farmer-centered challenges, sustainability claims, and responsible innovation. It does not treat technology adoption as a simple matter of farmer willingness or technical readiness. Instead, it situates adoption within broader arrangements of infrastructure, affordability, trust, knowledge, governance, and market pressure. The article contributes a critical synthesis of smart farming evidence and develops a forward-looking agenda for research and governance. It argues that smart farming futures should be designed around transparency, participation, interoperability, accountable digital systems, social equity, and ecological care. The review therefore reframes smart farming as a contested transformation whose benefits depend on how digital systems are governed, adopted, and made accountable.

IoT-enabled precision agriculture connects sensors, devices, communication networks, data platforms, and decision-support systems to support more responsive agricultural monitoring and management. These systems are increasingly discussed in relation to crop monitoring, irrigation management, greenhouse control, livestock monitoring, soil assessment, and farm-level advisory services. Their value, however, depends on how reliably connected technologies function under agricultural field conditions. The evidence base remains fragmented across architecture design, sensor deployment, connectivity models, data processing, and decision-support applications. Some studies emphasize layered IoT architectures, while others focus on wireless sensor networks, cloud platforms, edge computing, communication protocols, or analytics-enabled management systems. This fragmentation makes it difficult to compare implementation models or assess whether technical designs are sufficiently aligned with agricultural decision needs. This article systematically reviews IoT-enabled precision agriculture literature with attention to four core domains: architectures, sensor networks, connectivity models, and decision-support applications. The central review question asks how IoT-enabled precision agriculture has been designed and applied through these domains, and how technical choices shape implementation reliability, scalability, interoperability, energy efficiency, cybersecurity, and decision-support relevance. The review treats IoT not as an automatically beneficial technology, but as a socio-technical system whose usefulness depends on context, integration, and field performance. The review synthesizes peer-reviewed scholarship using explicit search concepts, eligibility criteria, study-selection logic, and evidence-mapping domains. The synthesis is organized around IoT architecture classification, sensor-network typology, connectivity model comparison, decision-support application mapping, implementation barriers, technical limitations, and future research directions. Because record-level search totals and exclusion counts were not available, the study-selection process is described narratively without fabricated PRISMA counts or statistical pooling. The review identifies recurring architectural patterns, sensor-network configurations, communication trade-offs, data-processing approaches, and decision-support applications in IoT-enabled precision agriculture. It also highlights persistent limitations involving interoperability, deployment scale, energy demand, data quality, field validation, cybersecurity, usability, and farmer-centered implementation. The article contributes an evidence-focused synthesis for designing more reliable, scalable, secure, and agriculturally meaningful IoT-enabled precision agriculture systems.