A Novel Blended Machine Learning Approach with TempoQA-Net for Crop Yield Prediction
Received: 27 March 2025 | Revised: 10 May 2025 | Accepted: 24 May 2025 | Online: 2 August 2025
Corresponding author: L. Narasimha Reddy
Abstract
Accurate crop yield prediction remains a significant challenge due to the complexity, non-linearity, and variability inherent in agricultural data, which are influenced by diverse climatic, geographic, and management factors. To address this, the present study introduces TempoQA-Net, a hybrid model integrating Long Short-Term Memory (LSTM) networks with Quantile Random Forest (QRF). Utilizing a comprehensive dataset of 12,834 records from the Special Data Dissemination Standard (SDDS) Division of the Indian Ministry of Agriculture, the model incorporates variables such as year, location, cultivated area, and climatic conditions. TempoQA-Net outperformed existing approaches, achieving a Mean Absolute Error (MAE) of 0.11995 kg/ha, a Mean Squared Error (MSE) of 0.02215 kg2/ha2, an R-squared value of 0.743, and an overall accuracy of 98.87%. These results demonstrate the model's robustness and predictive accuracy, providing a valuable tool for enhancing agricultural planning and policy development.
Keywords:
crop yield, accuracy, prediction, long short-term memory, agricultureDownloads
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