AI-Enhanced Precision Crop Rotation Management for Sustainable Agriculture

This paper presents a novel approach to crop rotation management that integrates cutting-edge artificial intelligence (AI) techniques to improve and modernise agricultural processes. The study seeks to revolutionise traditional crop rotation tactics by utilising state-of-the-art machine learning techniques, namely supervised learning approaches. Through the use of real-time weather forecasts and historical crop performance data, AI-powered technology provides farmers with tailored advice for maximising crop rotations, ultimately leading to better agricultural results. The methodical methodology includes planning the project, gathering data, creating AI models, and deploying the system. Establishing precise project objectives, collecting and analysing soil data with care, creating AI-driven recommendations, and making sure that monitoring and improvement are ongoing are all crucial tasks. At every level, the system uses a range of instruments and software to guarantee a methodical, data-driven approach that produces improved crop rotation techniques that are advantageous to the environment and the agricultural industry. This study adds to the rapidly developing field of artificial intelligence in agriculture by offering a workable and creative answer to the problems farmers face when managing crop rotation. The results underline how AI may be used to improve farming techniques, increase crop yields, and promote sustainable farming, which makes it an important contribution to the field of precision agriculture research.