Agrosmart Precision Soil Analysis Bot for Nutrient Prediction and Fertilizer Recommendations - Sandeep Kumar; Sanket Sharanappa Wali; Vinayak Tonne; Sudarshan D; Jyoti R Munavalli (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Agriculture, a key global economic sector, especially in India, suffers from low productivity due to climate variability, resource inefficiencies, and limited data, but precision agriculture, leveraging technologies like IoT, machine learning (ML), spectroscopy, and real-time analytics, proposes transformative solutions such as real-time soil analysis systems utilizing sensors like TCS34725 to measure soil nutrients (NPK) via RGB values processed by ML models like Random Forest, XGBoost, and LightGBM, with data delivered through user-friendly mobile apps built on Flutter and Firebase, enabling cost-effective, sustainable, and efficient crop and fertilizer recommendations, while IoT- and AI-based systems using ANN, linear regression, and GNB models further optimize fertilizer usage, enhance soil health, and improve yields by predicting soil properties like pH and environmental conditions, addressing food security and promoting sustainable practices with innovations like blockchain-based fertilizer delivery and robotics for autonomous farming tasks like soil assessment, disease diagnosis, and crop monitoring, as demonstrated in projects like "FertiForecast," "AI-Based IoT Framework for Smart Coconut Farming," and "Autonomous Agricultural Monitoring Robot," which integrate ML, IoT, and robotics for data-driven farming, tackling inefficiencies, empowering small-scale farmers, and advancing resource-efficient, sustainable agriculture globally.