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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.

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.

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.