IoT-Enabled Smart Soil Monitoring System with Crop and Fertilizer Recommendations

Soil health, influenced by variables such as temperature, moisture content, and nutrient levels, plays a crucial role in agricultural productivity. The proposed Smart Soil Monitoring System that integrates the Internet of Things (IoT) with machine learning to enable real-time soil analysis and provide actionable crop and fertilizer recommendations. The system utilizes various sensors, including NPK, soil moisture, temperature, and humidity sensors, interfaced with the Arduino UNO microcontroller. The MAX485 module ensures reliable data transmission, while an LCD module displays local soil parameter measurements. Data is sent to the cloud using the ESP8266 micro controller, where it is continuously monitored, pre-processed, and analyzed. The Random Forest algorithm is employed to train a machine learning model using key soil variables identified through a feature extraction process. The FastAPI framework efficiently processes the data in the cloud, delivering real-time recommendations for optimal crops and fertilizers based on specific soil conditions. System performance is evaluated using metrics like accuracy and efficiency, demonstrating predictive reliability with 90% accuracy in crop prediction. By combining IoT and machine learning, the proposed system offers a scalable and cost-effective precision agriculture solution, providing farmers with data-driven insights to enhance agricultural yields, minimize resource wastage, and promote sustainable farming practices.