Integrated ARIMA and Multi-Scale GRU for Crop Recommendation and Yield Prediction in Precision Agriculture

Abstract

Agriculture is the fundamental source of food, income, and livelihood for rural communities in India. Numerous crops are affected due to the lack of technical decision-making support and variations in weather patterns, temperature, rainfall, and atmosphere factors, which play a critical role in defining crop yield. Hence, choosing the appropriate crop to maximize yield is key to enhancing real-time farming practices. This study proposes an Auto-Regressive Integrated Moving Average (ARIMA) and Multi-Scale Gated Recurrent Unit (MSGRU) model for effective crop yield prediction and crop recommendation. Initially, label encoding and min-max normalization techniques are applied during the pre-processing phase to transform categorical values into uniform, numerical format for data scaling. Then, ARIMA is employed for crop yield prediction, followed by the MSGRU network deployed to extract both short-term and long-term dependencies, enabling accurate crop recommendation. The proposed ARIMA–MSGRU model achieves a superior accuracy of 99.73% at a reduced RMSE of 1.568, outperforming existing algorithms, demonstrating greater effectiveness.