Predicting Crop Yields: Harnessing IoT Sensor Data for Accurate Forecasting and Sustainable Agricultural Planning

Abstract

This research introduces a novel approach to precision agriculture by integrating IoT technology and machine learning algorithms to enhance crop health prediction, suitability forecasting, and crop yield prediction for rice, maize, mango, and watermelon. Utilizing real-time data from infield sensors monitoring soil moisture, nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall, the Agro-Sense system enables continuous and dynamic crop monitoring. This innovative system provides timely, data-driven insights previously unavailable with traditional models. Machine learning algorithms, specifically XGBoost and RandomForestClassifier, predict crop health and forecast suitability with improved accuracy over conventional methods. By incorporating real-time sensor data, the Agro-Sense mobile app optimizes nutrient management, reducing fertilizer waste. This research addresses the limitations of previous models, which often rely on static datasets and fail to account for diverse environmental factors. Unlike conventional methods, Agro-Sense continuously adjusts predictions based on evolving environmental parameters, making it highly adaptable to different crops and regions. The system's specific health assessments and suitability forecasts offer farmers actionable insights for informed, sustainable agricultural decisions. Agro-Sense presents a scalable, IoT-driven framework that bridges gaps in existing crop prediction methods, enhancing both crop health prediction and suitability forecasting for sustainable farming practices. Future advancements should expand predictive capabilities to optimize resource use and enhance crop health across diverse environments.