Abstract
India, often referred to as an agricultural country, has 52% of its population engaged in agriculture. The agricultural sector's prosperity is heavily dependent on regional climatic conditions. Adverse weather events significantly impact farmers' economic stability, exacerbating existing pressures and contributing to high rates of farmer suicides. This paper proposes a novel solution: the integration of regression-fuzzy models for predicting the economic conditions of farmers. By utilizing historical weather data spanning the past decade, this model aims to forecast economic fluctuations and mortality rates among farmers. The primary objective is to reduce the mortality rate by providing accurate predictions based on weather impacts on crop production, specifically focusing on Kharif and Rabi seasons. Through the application of fuzzy clustering and rule generation, the model classifies farmers' economic conditions and assesses the influence of weather on their production. This approach offers actionable insights to enhance economic resilience and mitigate distress in the agricultural sector.
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