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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.

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.

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.

Achieving food availability, advancing the long-term viability of farming, and propelling economic progress. However, current forecasting frameworks often overlook the specific characteristics of rice yield distributions, which vary across regions, exhibit skewness, and are constrained by fixed lower and upper thresholds, conforming to a four-parameter beta distribution. The consequence is outputs that are less reliable and can potentially misguide decision-making. Furthermore, a majority of techniques fall short in modeling the intricate variable interactions that arise when satellite observations are fused with field survey measurements, an integration widely viewed as essential for boosting forecast fidelity and streamlining cultivation methods. To remedy these deficiencies, the present research proposes a four-parameter beta Generalized Linear Mixed Model (GLMM) and advances it into a four-parameter beta Generalized Mixed Effect Tree (GMET). This four-parameter beta GMET extends the four-parameter beta GLMM architecture by embedding a regression tree mechanism, thereby providing superior flexibility for representing non-linear and interaction-laden dependencies. Deploying this analytical framework on empirical data from Central Kalimantan and Karawang yields significant gains over earlier approaches, as evidenced by markedly lower AIC and RRMSE metrics. Significantly, the investigation finds that time-lagged reflectance values from bands 4 and 8 and the NDVI index captured by Sentinel-2A satellites are the dominant determinants of rice productivity, overshadowing the explanatory contribution of farmer questionnaire responses. This finding underscores the promise of remotely sensed data as a cornerstone for rice yield estimation, offering a more streamlined, less resource-intensive alternative to reliance on farmer-reported data. By further refining satellite-based technologies, upcoming initiatives in yield forecasting can realize superior efficiency and precision, thereby underpinning evidence-based choices in agricultural governance.

Fungi represent a highly nutritious dietary staple that can be essential for supplying cost-effective protein, vitamins, and minerals to populations around the globe; however, their successful cultivation requires extensive training and specialized knowledge to carefully calibrate a range of environmental variables. Internally displaced populations located in Northern Syria depend on extremely limited traditional oyster mushroom farming, which falls short of meeting local consumption requirements. Numerous international and local non-governmental organizations (NGOs) supporting Syrian refugees run mushroom-farming programs. They have encountered considerable difficulties and obstacles in mushroom farming among the intended participants. Thus, the two core questions steering this investigation are: (1) How might organic mushroom farming be advanced via a reliable and low-cost intelligent cultivation system? (2) In what ways can organic mushroom farming methods be streamlined to assist internally displaced persons and Syrian refugees? This investigation examines the mechanization of mushroom farming by designing and implementing an intelligent oyster (Pleurotus ostreatus) cultivation system for remote surveillance and adjustment of environmental conditions—temperature, humidity, air quality, and light—within the growing space. Additionally, ready-made and purpose-built, intuitive web interfaces were created to enable growers to survey and operate their farms via the Internet remotely. Consequently, a trustworthy and economically viable intelligent oyster mushroom cultivation system was designed and successfully implemented in this study. The system encompasses remote surveillance and operation via intuitive interfaces. This makes mushroom farming more straightforward not only for displaced populations and refugees but also for mushroom growers in nations with limited financial resources. This research can aid in the elimination of poverty and hunger, in accordance with the United Nations Sustainable Development Goals one and two.

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.

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.

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.

Precision farming increasingly depends on diverse agricultural data sources, yet these sources are often discussed as isolated technologies rather than as complementary inputs within decision-support systems. This article develops a conceptual taxonomy of agricultural data sources for precision farming by classifying remote sensing, geospatial layers, IoT sensors, machinery-generated records, climate and weather data, soil data, environmental observations, farm-management records, and farmer knowledge. The taxonomy approach is literature-grounded and organized around data-generation mechanism, spatial and temporal scale, resolution, format, uncertainty, interoperability requirements, ownership concerns, farmer usability, decision-support relevance, and analytics readiness. Rather than treating data as automatically useful, the article argues that each source family has distinct strengths, limitations, validation needs, and practical conditions of use. Remote sensing can support spatial monitoring, IoT sensors can provide high-frequency observations, machinery records can document operational activity, climate and soil data can contextualize crop response, and farmer knowledge can support interpretation, feasibility assessment, and local validation. The contribution is an original data-source taxonomy that helps researchers, advisors, farmers, and technology developers evaluate how agricultural data sources can be combined responsibly for precision farming. The taxonomy supports more coherent data integration, more transparent AI-ready agricultural systems, and more farmer-centered decision support.

Digital twins are increasingly discussed as a next step in smart farming because they promise to connect physical agricultural systems with digital representations, sensor data, simulation models, analytics, and decision-support functions. However, the agricultural literature remains uneven in how it defines, architects, enables, and applies digital twins across crop, livestock, greenhouse, irrigation, and farm-management systems. This original scoping review maps the peer-reviewed literature on digital twins in smart farming with attention to architecture types, enabling technologies, data requirements, application domains, implementation challenges, and research gaps. The review follows a scoping logic rather than an effect-estimation or performance-benchmarking design: it charts how studies conceptualize digital twins, distinguishes full or emerging digital twin capability from static models and monitoring dashboards, and classifies evidence by architecture, technology, data pipeline, analytics function, feedback mechanism, implementation stage, and limitation. The synthesis shows that agricultural digital twins are commonly positioned as cyber-physical and decision-support infrastructures that may combine IoT sensing, remote sensing, machinery and environmental data, cloud or edge computing, simulation, AI analytics, visualization, and farm-management interfaces. Application clusters are strongest in controlled-environment agriculture, irrigation and water management, crop monitoring, aquaponics, livestock monitoring, greenhouse visualization, and emerging optimization or automation settings. Across the mapped literature, the main contribution is a structured classification of digital twin architectures and application patterns while preserving conceptual caution: real-time data, AI prediction, or visualization alone does not create a digital twin. The review identifies future research needs in architecture standardization, semantic interoperability, field validation, real-time updating, uncertainty communication, data governance, farmer-centered usability, scalability, and sustainability assessment.

Sustainable precision agriculture increasingly depends on the convergence of artificial intelligence, robotics, automation, and Internet of Things technologies rather than on isolated digital tools. This integrative review synthesizes how these technology domains interact across sensing, data transmission, analytics, decision support, robotic or automated actuation, and feedback within sustainability-oriented farming systems. The review uses a curated peer-reviewed literature selection and an integrative evidence-charting logic to map technology domains, agricultural applications, integration mechanisms, decision functions, sustainability relevance, and system-level challenges. The AI focus includes machine learning, deep learning, computer vision, predictive analytics, data fusion, and decision-support models used for crop monitoring, yield estimation, input optimization, pest and disease detection, and risk forecasting. The robotics and automation focus covers autonomous field robots, UAVs, smart machinery, task execution, and automated farm operations. The IoT and sensing focus includes wireless sensor networks, distributed monitoring, machinery telemetry, remote sensing, cloud and edge computing, and data platforms. The synthesis shows that technology convergence may support resource-efficient, climate-sensitive, and more responsive farm management, but only when data quality, interoperability, field robustness, explainability, affordability, governance, usability, and farmer or advisor feedback are addressed. The review contributes a system-level framing of sustainable precision agriculture as a socio-technical feedback system in which digital sensing, AI interpretation, automated action, and human decision-making must be jointly designed. Future research should prioritize validated, interoperable, transparent, and farmer-centered integration pathways rather than assuming that more automation or more data automatically improves sustainability outcomes.