LoRa-Enabled Semi-Autonomous Rover for AI-Driven Crop Prediction Using Data-Driven Decision-Making for Tamil Nadu Agriculture

Abstract

This research presents a LoRa-enabled semi-autonomous rover system designed for real-time environmental and soil parameter monitoring to facilitate data-driven decision-making (DDDM) in agriculture. The rover is equipped with multi-modal sensors, including soil moisture, temperature, pH, NPK, electrical conductivity (EC), light intensity, wind speed, and rainfall sensors, to capture high-resolution field data. Utilizing LoRa communication, these data points are transmitted to a cloud-based server for processing and analysis. A Random Forest-based AI model is employed to correlate real-time sensor data with historical agricultural datasets from Tamil Nadu, enabling predictive analytics for crop selection and soil health assessment. The system generates data-driven agronomic recommendations, assisting farmers in optimizing crop yield, resource utilization, and sustainable farming practices. A dashboard-based web interface provides intuitive visualizations and insights, ensuring accessibility and informed decision-making. This IoT-integrated precision agriculture framework enhances spatial and temporal data analysis for improved agricultural productivity. By leveraging AI-driven analytics and real-time monitoring, this solution contributes to predictive farming, optimized land use, and enhanced food security, fostering a sustainable and technologically advanced agricultural ecosystem.