Intelligent Data Mining Framework for Precision Agriculture and Crop Yield Forecasting

Abstract

The application of data mining in agriculture has emerged as a transformative tool in enhancing decision-making and improving crop yield forecasting. This study presents an intelligent data mining framework that leverages classification, clustering, and association rule mining to analyze heterogeneous agricultural datasets. By integrating these techniques within a decision support system, the framework enables early prediction of crop performance and cost-benefit analysis, facilitating informed decision-making for farmers and policymakers. The proposed model supports site-specific management, resource optimization, and seasonal forecasting using a combination of historical data and predictive algorithms. Experimental results on crops such as potato, brinjal, tomato, and okra across multiple seasons demonstrate improved accuracy in yield and market price predictions. The findings highlight the potential of data-driven agriculture in fostering sustainability and resilience in the agri-economy.