IoT-Driven Smart Farming with Machine Learning for Sustainable Food Systems

Integrating the Internet of Things (IoT) and machine learning (ML) technologies in agriculture, commonly called smart farming, is revolutionizing the sector by enhancing productivity, efficiency, and sustainability. This paper explores the application of IoT-driven smart farming using machine learning for sustainable agricultural practices. The system introduces an efficient Soil Moisture Detection System utilizing IoT Technology, revolutionizing modern farming practices. By continuously monitoring crucial parameters such as soil moisture, temperature, and humidity in real-time, the system ensures seamless data transmission to a centralized server. Additionally, integrating motion detection capabilities enhances security measures and promptly alerts farmers to environmental changes. The dataset consisting of 100,000 rows is generated to facilitate the development and training of five ML models to predict soil moisture trends. Decision Trees achieved an accuracy rate of 99.98%, while Random Forests achieved 99.99%. The integration of these predictive models empowers farmers with actionable insights for precise irrigation scheduling and optimal crop yield optimization. These models provide actionable insights for precise irrigation scheduling and optimal crop yield optimization. Field tests have confirmed the efficacy of this approach, demonstrating significant improvements in irrigation efficiency and subsequent crop yields. Thus, the proposed system represents a substantial advancement in leveraging the synergistic potential of IoT and ML technologies to foster sustainable agricultural practices.