AI Precision for Irrigation, Crop Management, and Pest Control for Sustainable Agriculture in Oman

Abstract The integration of artificial intelligence (AI) precision techniques can improve sustainable agriculture practices in Oman. Our research aims to optimize crop management, pest control, and irrigation techniques by utilizing datasets from the Integrated Crop and Agricultural Resource Data System (ICARDS) and the Integrated Digital Framework for Irrigation Practices (IDFIP). The foundation for well-informed decision-making is provided by the extensive data on soil conditions, weather patterns, and past agricultural practices found in the IDFIP and ICARDS datasets. Our methodology uses Python- implemented machine learning models and sophisticated algorithms. The data is analysed by AI-driven models, which provide useful information for targeted pest control, effective crop management, and precision irrigation. Through the practical use of AI approaches, this research advances the field of precision agriculture. The results could greatly increase agricultural output, preserve water supplies, and reduce their negative effects on the environment. The Sultanate of Oman’s agriculture sector faces numerous obstacles, but integrating AI precision technologies offers a viable way to promote sustainable practices and guarantee food security.