AI AND IOT APPLICATIONS IN SMART AGRICULTURE

Abstract: Smart agriculture is evolving from conventional, experience-based farming toward data-driven and automated decision making. The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), often termed Artificial Intelligence of Things (AIoT), enables continuous sensing, communication, prediction and control across the agricultural lifecycle. IoT devices collect soil moisture, temperature, humidity, nutrient, weather and crop- condition data, while AI models transform these observations into recommendations or automated actions. Recent studies show applications in precision irrigation, crop and yield prediction, soil and nutrient management, pest and disease detection, greenhouse and hydroponic control, weed management, livestock monitoring, post-harvest management and climate-smart farming. This paper reviews research published, Summarizes major AI-IoT approaches, identifies gaps in scalability, interoperability, data quality, connectivity, explainability, cyber security and smallholder adoption, and outlines future directions involving edge AI, federated learning, digital twins, 5G/6G, low-cost virtual sensing and multimodal foundation models. The evidence indicates that AI-IoT can improve resource efficiency and resilience, but practical deployment requires affordable, robust and farmer-centered systems rather than isolated laboratory prototypes.

Keywords: Artificial Intelligence, Internet of Things, AIoT, Smart Agriculture, Precision Agriculture, Machine Learning, Edge Computing, Crop Monitoring, Smart Irrigation