Optimizing Sustainable Agriculture in Oman Through AI-Driven Precision Techniques

In this proposed approach, we describe the transformative potential of AI-driven precision techniques to optimize sustainable agriculture in Oman. For this research proposal, we have used datasets from the Integrated Crop and Agricultural Resource Data System (ICARDS) and the Integrated Digital Framework for Irrigation Practices (IDFIP). The aim of our proposed approach is to enhance crop management, pest control, and irrigation efficiency. By studying these datasets, we can get substantial information on various soil conditions, the weather patterns that can affect agriculture productivity, and regarding the historical agricultural practices, which can form the basis for the AI-model for certain informed decision-making. Implementation of these machine learning models and advanced algorithms, AI-driven analysis can provide actionable insights for targeted pest control, precision irrigation, best agricultural practices and higher yield. The research results show that the application of AI in agriculture can significantly detect croprisks, boost crop yields, conserve water resources, and minimize environmental impacts. This research addresses the challenges faced by Omani farmers, and hence proposes the AI precision technology as a viable solution for ensuring food security in the region. The proposed approach will contribute to revolutionize Oman's agricultural landscape, economic stability, food security aiming sustainable development and economic diversification.