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

This research addresses the gap in precision agriculture by leveraging IoT technology to enhance crop yield prediction and health monitoring. Agro-Sense integrates real-time data from in-field sensors measuring key parameters such as soil moisture, nitrogen, pH, temperature, and humidity, providing a data-driven solution for rice, maize, mango, and watermelon. This approach overcomes the limitations of traditional models that often fail to consider diverse environmental factors. Machine learning algorithms, specifically XGBoost and RandomForestClassifier, were employed to forecast crop yield and health status. XGBoost outperformed RandomForestClassifier in accuracy, demonstrating superior prediction performance, and served as a benchmark for model robustness. The research highlights the importance of continuous data collection and quality-focused preprocessing to support reliable and robust predictive modeling. By incorporating advanced machine learning techniques, Agro-Sense offers specific health assessments for each crop, enabling real-time, data-driven decision-making. This system is adaptable to varying environmental conditions and can be easily integrated into existing farming practices. The tool empowers farmers by providing efficient, sustainable agricultural practices, improving resource use, and enhancing crop health. Agro-Sense bridges the gap in current crop prediction methods and creates a scalable, IoT-driven framework for agriculture. Future advancements should further optimize resource management, enhance crop health prediction, and support sustainable farming in diverse environments globally.