SAMS: Smart Agricultural Monitoring System Integrating Edge AI and IoT for Early Detection of Rice Crop Stress

This paper presents a Smart Agricultural Monitoring System that integrates Edge AI and IoT technologies for early detection of rice crop stress. The proposed system aims to improve agricultural productivity by providing timely insights into crop health. By leveraging Edge AI, the system can analyze data locally on edge devices, reducing latency and bandwidth requirements. IoT devices are deployed in the field to collect data on various parameters such as soil moisture, temperature, and humidity. The collected data is processed using machine learning algorithms to detect signs of crop stress, enabling farmers to take proactive measures to mitigate potential losses. The effectiveness of the system is demonstrated through experimental results and case studies.