AI and IoT-Driven Soil Health Restoration: A Machine Learning Approach for Sustainable Agriculture

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is redefining soil health monitoring, ushering in a new era of intelligent, data-driven agriculture. This paper explores the cutting-edge integration of AI and IoT technologies, detailing sensor-driven real-time data collection, advanced data transmission methods, and machine learning algorithms for soil classification and predictive modeling. Beyond conventional applications in precision agriculture—such as smart irrigation and optimized nutrient management—this study delves into transformative innovations, including remote sensing and eco-acoustics, poised to revolutionize soil assessment. A novel Random Forest machine learning model implementation achieves an unprecedented 99% accuracy in soil health classification, demonstrating a groundbreaking approach to predictive soil restoration. By tackling challenges in sensor efficiency, data standardization, and cost-effective deployment, this research highlights the game-changing potential of AI-IoT ecosystems in fostering sustainable agriculture. These advancements pave the way for a future where technology-driven insights empower farmers, enhance resource efficiency, and ensure global food security.