Soil Moisture and Crop Management using Machine Learning and Embedded System

Traditional agricultural practices often rely on manual observations and fixed irrigation schedules, leading to inefficient water usage, reduced crop yields, and limited scalability. Farmers lack real-time data-driven insights to make timely decisions regarding irrigation and crop health. The absence of automation and predictive analysis in rural farming contributes to inconsistent productivity and wastage of resources. This paper proposes an intelligent soil moisture and crop management system that uses embedded systems to collect environmental data such as soil moisture, temperature, and humidity, and employs machine learning models to analyze this data and predict irrigation needs. The system improves water usage efficiency, promotes sustainable farming practices, and reduces human efforts. The embedded system is designed to be compact, low-power, and user-friendly, supporting both offline and online operation modes. For remote monitoring, data is transmitted via Wi-Fi or GSM modules to a mobile application interface, where farmers can view insights, receive alerts, and manually override automatic irrigation systems if needed. The methodology integrates precision agriculture techniques through advanced agricultural sensors, showcasing how AI and IoT technologies can transform traditional agriculture into a smarter and resource- efficient system.