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Presently, the agricultural domain faces major hurdles in efficiently monitoring livestock health. Established observation practices tend to be labor-heavy, slow, and ill-suited to the evolving requirements of contemporary farming. The rapid proliferation of IoT devices in this field has brought scalability issues and increased processing demands to the forefront, creating an urgent need for streamlined, scalable architectures. This work proposes a cloud-centric framework to elevate the standard of livestock wellness tracking. Engineered to monitor key physiological metrics such as motility, thermal state, and cardiac rhythm, the setup leverages AWS for resilient data orchestration, alongside Python for analytical computation and live-stream analytics. The framework embraces Narrow Band IoT (NB-IoT) mechanisms, fine-tuned for minimal bandwidth usage and extended transmission range, which are ideal for isolated agricultural terrains. Its scalable design enables seamless administration of a dynamic number of IoT devices, a vital feature for accommodating changes in livestock density and operational scale. Early-stage testing conducted to gauge system capability has confirmed its resilience and practical value, reflecting a cohesive integration of AWS IoT Cloud capabilities with field-level IoT devices. Beyond this, the inquiry examines the deployment of predictive algorithmic models to support anticipatory healthcare strategies in animal populations. By forecasting impending health anomalies before visible symptoms, the mechanism stands to advance animal well-being and farm output substantially. The confluence of cloud computing power and IoT infrastructure not only meets the intensifying digital demands of modern agriculture but also pioneers a new paradigm for sustainable ecological farming methods. The revelations from this investigation may carry far-reaching weight for the trajectory of animal stock oversight, potentially catalyzing broad-based acceptance of digitally driven wellness surveillance frameworks across the farming sector. Such a shift would aid in streamlining husbandry-based health management worldwide, thereby enhancing yields and long-term viability in the agrarian economy.

Agricultural production is affected by plant diseases, affecting both quality and quantity. Yet, forecasting these diseases demonstrates its value for crop quality and reducing the chance of yield losses. The process of detecting plant diseases—whether by the naked eye or through conventional methods—is generally painstaking, time-consuming, and subject to substantial margins of error. This work presents a comprehensive exploration of diverse CNN architectures paired with distinct optimization algorithms, applied to olive disease identification via classification methods, and identifies the optimal model for an efficient disease detector. The exploration presents a collection of 5571 olive leaf images, gathered by hand in field settings across different regions of Morocco, and additionally includes a healthy class to identify olive diseases. Moreover, a primary aim of this research was to explore the interplay between CNN architectures and optimization algorithms as measured by accuracy and additional performance indicators. The peak score in the trained models reached 100%, while the peak score in experiments without data augmentation was 92.59%. A further emphasis of this exploration is the effect of optimization algorithms on neural network functioning. Based on the experiments, the MobileNet architecture with Rmsprop outperformed the other pairings in both detection performance and efficiency.

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.

The convergence of artificial intelligence and the Internet of Things is creating new possibilities for adaptive farming systems. In agricultural settings, this convergence allows sensor networks, machine learning models, communication infrastructures, and automated decision support tools to operate as interconnected cyber-physical systems. Yet many current smart farming architectures remain technically fragmented and do not fully connect operational decisions with sustainability feedback. A central problem is that sensing, intelligence, agronomic reasoning, and sustainability assessment are often treated as separate system layers. Sensor data may support monitoring, machine learning may support prediction, and sustainability indicators may be reported after implementation, but these functions are rarely organised as a continuous adaptive loop. This separation limits the ability of farming systems to learn from environmental consequences and adjust decisions in real time. This article proposes a conceptual architecture for AIoT-enabled adaptive farming systems. The architecture integrates distributed sensor networks, edge-based intelligence, agronomic knowledge models, and sustainability feedback mechanisms into a closed-loop system. It is designed for farming environments where decisions must be locally responsive, scientifically grounded, and aligned with long-term resource stewardship. The article is based on a conceptual design synthesis of peer-reviewed literature published. The synthesis draws on agricultural IoT, edge computing, machine learning, crop monitoring, remote sensing, smart farming platforms, and sustainability-oriented digital agriculture. No new empirical data are presented. The resulting architecture specifies four interacting layers: sensing, edge intelligence, agronomic knowledge integration, and sustainability feedback. It defines how sensor observations move through edge analytics and knowledge-informed decision support before being evaluated against sustainability indicators that feed back into future control actions. The article contributes a systems blueprint for adaptive farming architectures that are not only intelligent and data-driven but also environmentally responsive and sustainability-oriented.

Cold chains are essential food-system infrastructures because they preserve safety, quality, nutritional value, and marketability from production to consumption. Despite their importance, many cold-chain operations still depend on reactive checks, delayed audits, and fixed temperature protocols that are poorly aligned with the biological variability of food products. This reactive orientation allows quality deterioration to accumulate invisibly until products arrive at inspection points, retailers, or consumers. The central problem addressed in this article is the absence of an integrated architecture that connects continuous monitoring, predictive analytics, and automated decision support across the full cold-chain pathway. Sensors may detect temperature excursions, models may estimate shelf life, and logistics systems may manage routing, yet these capabilities often remain fragmented. Without architectural integration, cold chains cannot become truly intelligent, adaptive, or self-correcting. The objective of this article is to propose an Intelligent Cold Chain conceptual architecture for real-time quality preservation, spoilage prediction, and loss prevention. The architecture is designed to transform environmental and logistical data into predictive quality intelligence and operational decisions. It positions the cold chain as a cyber-physical system in which sensing, prediction, decision-making, and coordination interact continuously. The article develops its architecture through a conceptual synthesis of peer-reviewed research published between 2017 and 2025. The synthesis draws from cold-chain logistics, food quality engineering, IoT monitoring, machine learning, digital twins, and supply-chain systems design. It does not report new empirical data, but instead integrates existing technological and theoretical insights into an original systems framework. The proposed architecture defines four functional layers: sensing, prediction, decision, and coordination. These layers specify how sensor streams are acquired, how spoilage risk is inferred, how corrective actions are recommended, and how information is shared across actors. The architecture provides a blueprint for moving cold chains from passive refrigerated corridors toward proactive, data-driven systems that preserve quality and reduce food losses.

Food security early warning systems increasingly draw on artificial intelligence, remote sensing, predictive analytics, climate information, crop monitoring, market indicators, and vulnerability data to identify emerging risks before they become severe crises. These systems are expected to provide timely signals about threats to food availability, access, utilisation, and stability across diverse ecological, economic, and social settings. Yet many early warning approaches remain fragmented. Climate risk, market instability, crop stress, and human vulnerability are often analysed in separate technical workflows, which can weaken interpretation, delay action, or produce alerts that are insufficiently linked to response capacity. This article develops a conceptual architecture for AI-enabled food security early warning systems. The architecture treats early warning as a layered process that connects data ingestion, risk-domain analytics, signal integration, uncertainty interpretation, alert generation, decision support, response targeting, and feedback governance. The proposed architecture explains how multi-source data and predictive models can be translated into interpretable and actionable food security warnings. It also emphasises that AI-enabled early warning can fail when data are incomplete, model outputs are opaque, vulnerable populations are underrepresented, or alerts are disconnected from institutions capable of acting.