Abstract:

AgriForecast presents a comprehensive predictive system for estimating crop yields and market prices using advanced machine learning techniques, including Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Decision Trees. The system integrates historical agricultural data, climatic conditions, and market trends to generate accurate forecasts, enabling farmers and policymakers to make data-driven decisions. Experimental evaluation demonstrates that LSTM and RNN models effectively capture temporal dependencies in crop growth patterns, while Decision Trees provide interpretable insights into feature importance. The proposed framework significantly improves prediction accuracy compared to traditional statistical approaches, facilitating sustainable agricultural practices and enhanced economic planning.

Description:

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