aspg-18-3001-1792025814 20261015005654 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 13 2 K M Monica Assistant professor, School of Computer Science and engineering. VIT, Chennai Anitha D Department of Computational Intelligence, School of computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India.603203 S.Prabu Professor, Department of ECE, Mahendra Institute of Technology, Namakkal, India B.Girirajan Assistant Professor, Department of ECE, SR University, Warangal, Telangana State Arun M Assistant Professor, ECE, Panimalar Engineering College, Chennai The agricultural industry faces significant challenges in improving efficiency and productivity, particularly in monitoring crop health and environmental conditions. Traditional methods are often labor-intensive, time-consuming, and lack real-time data, leading to suboptimal decision-making. Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies offer promising solutions. Long Range (LoRa) communication, a type of low-power wide-area network (LPWAN), enables long-distance data transmission with minimal power consumption, making it ideal for rural and expansive agricultural areas. When combined with deep learning, which can analyze large volumes of data to generate predictive insights, these technologies have the potential to revolutionize agricultural practices by providing farmers with timely and accurate information to optimize crop management and resource utilization. This study introduces an intelligent mote for agricultural applications, leveraging Long Range (LoRa) communication and deep learning techniques to improve precision farming. Traditional agricultural monitoring methods are labor-intensive and lack real-time insights. To address this, the mote is equipped with sensors to monitor temperature, humidity, soil moisture, and light intensity, transmitting real-time data over long distances with minimal power consumption using LoRaWAN. The collected data is processed by deep learning models to predict crop yield and identify potential issues. Field tests demonstrated a 15% improvement in yield prediction accuracy and a 20% reduction in water usage compared to traditional methods. These results highlight the effectiveness of integrating LoRa and deep learning in enhancing agricultural resource management and productivity. 2024 178 191 3001 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.130214 https://americaspg.com/journal/18/article/3001 https://americaspg.com/storage/31720373754.pdf