Fuzzy Goal Programming with Artificial Intelligence Enhancement to Plan ClimateResilient Crop under Uncertainty in Indian Agriculture

Adaptive and data-driven optimization frameworks are needed in sustainable crop planning in the context of climatic and market uncertainty. The conventional models of fuzzy goal programming (FGP) tackle multi-objective agricultural decision-making, but it is based on constant parameters and predetermined aspiration levels, implying that it is not responsive to changes in the environmental conditions. This paper introduces an Artificial Intelligence-based Fuzzy Goal Programming (AI-FGP) model to get optimal crop distribution in the case of unpredictable rainfall and subject to market fluctuations. Machine learning models are utilized to forecast rainfall, crop yield and market prices and these forecasts are integrated into a fuzzy multi-objective optimization design. The parameters being predicted update dynamically the membership functions and priorities of the goals allowing adaptive decision-making. An examination of rice, wheat, and pulses across 100 hectares showed that the suggested model enhances the total goal satisfaction and efficiency in water use in comparison to classical FGP. The findings suggest that predictive analytics combined with fuzzy optimization will help to improve agricultural planning robustness and sustainability.