IoT Data and Random Forest Algorithm for Optimized Crop Rotation Planning for Sustainable Agriculture

Modern farming has prioritized developing sustainable agricultural practices to combat food insecurity and environmental damage. A reliable strategy for maintaining healthy soil and increasing yields is crop rotation. This study proposes a novel method that integrates Internet of Things (IoT) data with the Random Forest algorithm to optimize crop rotation plans. IoT sensors are real-time monitoring of the soil’s moisture, temperature, and nutrient content using IoT, and a comprehensive overview is presented by combining this data with historical records and predictions. This dataset is analyzed using the Random Forest technique, which considers various characteristics, including crop compatibility, soil conditions, and climatic trends. The program uses machine learning to create optimal crop rotation plans for productivity, pest and disease control, and the promotion of sustainable agriculture. Our findings show that this strategy enhances crop rotation planning, increasing agricultural output while decreasing its ecological footprint. It helps in the development of sustainable farming methods and the expansion of precision agriculture via the use of $\mathrm{Io} T$ and machine learning.