AI-DRIVEN FRAMEWORK FOR CROP SELECTION INTEGRATING YIELD AND SEASONAL FACTORS FOR ENHANCED PRODUCTIVITY

Abstract

Global food security depends to some extent on agricultural systems, but changing environmental conditions and unstable production conditions are presenting challenges for farmers. Traditional systems that once had great importance are becoming ineffective due to uncertain weather conditions and changing agricultural conditions. With the development of data-driven solutions, the integration of machine learning and data mining has emerged as a powerful vision that can help farmers. These technologies analyze historical and environmental data to recommend the best decisions based on weather, soil conditions, water availability and market trends. Crop research systems not only help farmers choose high-yielding crops, but also promote sustainable farming systems. Using such measures, farmers can improve production, manage resources more efficiently and reduce the risks associated with climate change. The goal is to help rural communities eliminate inequalities, promote smarter decisions and contribute to better food production..