DualSpinNet: A crop yield prediction model based on LSTM and GRU

Accurate prediction of crop yield is of great significance for agricultural production and food security. In this paper, we introduce DualSpinNet, a novel model that integrates long short-term memory network (LSTM) and gated recurrent unit (GRU) architectures to address this challenge. This model employs a dual-stream approach to extract temporal features of climate and soil data, utilizing parallel LSTM and GRU layers. These features are subsequently refined through an additional GRU layer to enhance time-series dependencies. The dual recurrent structure allows for more precise extraction and processing of multi-level temporal features, thereby improving the accuracy of prediction. The final yield predictions are generated through a fully connected layer. We trained and validated the model using the Kaggle dataset, and compared its performance with other state-of-the-art models. Empirical results demonstrate that our proposed model achieves lower M S E and M A E , making it effective for crop yield prediction. The proposed method offers a highly accurate tool for agricultural producers and decision-makers, contributing to improvements in crop yield and quality, and promoting food security and sustainable agricultural development.