Abstract
Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among climatic, soil, and environmental factors. This study introduces DeepAgroNet, a novel three-branch deep learning framework that integrates satellite imagery, meteorological data, and soil characteristics to estimate winter wheat yields at the district level in southern Pakistan. The framework employs three leading deep learning models-convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)-trained on detrended yield data from 2017 to 2022. The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. CNN emerged as the most effective model, achieving an R(2) value of 0.77 and a forecast accuracy of 98% one month before harvest. The RNN and ANN models also demonstrated moderate predictive capabilities, with R(2) values of 0.72 and 0.66, respectively. The results showed that all models achieved less than 10% yield error rates, highlighting their ability to effectively integrate spatial, temporal, and static data. This study emphasizes the importance of deep learning in addressing the limitations of traditional manual methods for yield prediction. By benchmarking the results against Crop Report Services data, this study confirms the reliability and scalability of the proposed framework. The findings demonstrate the potential of DeepAgroNet to improve precision agriculture practices, contributing to food security and sustainable agricultural development in Pakistan. Furthermore, this adaptable framework can serve as a model for similar applications in other agricultural regions around the world.