Abhasha Joshi ¹ Biswajeet Pradhan ^1* Subrata Chakraborty ² Renuganth Varatharajoo ³ Abdullah Alamri ⁴ Shilpa Gite ⁵ Chang-Wook Lee ⁶

• ¹ School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia
• ² School of Science and Technology, Faculty of Science, Agriculture, Buiness and Law, University of New England, Armidale, Australia
• ³ Department of Aerospace Engineering, Faculty of Engineering, Putra Malaysia University, Serdang, Malaysia
• ⁴ Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia, Riyadh, Saudi Arabia
• ⁵ Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India, Pune, Maharashtra, India
• ⁶ Department of Science Education, Kangwon National University, Chuncheon-si, 24341, Korea, Chuncheon-si, Republic of Korea

Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored. Interpretability is another pressing and challenging issue in DL-based crop yield prediction, a factor that ensures the reliability of the model. Thus, this study aims to develop and implement an explainable DL model capable of accurately predicting crop yield and providing explanations for the predictions. To achieve this, we developed three state-of-the-art sequential DL models: LSTM, 1D CNN, and Bi-LSTM. We then employed three popular interpretability techniques: Local interpretable model-agnostic explanations (LIME), Integrated Gradient (IG) and Shapley Additive Explanation (SHAP) to understand the decision-making process of the models. The Bi-LSTM model outperformed other models in terms of predictive performance (R2 up to 0.88) and generalizability across locations and ranges of yield data. Explainability analysis reveals that enhanced vegetation index (EVI), temperature and precipitation at later stages of crop growth are most important in determining Winter wheat yield. Further, we demonstrated that XAI methods can also be used to understand the decision-making process of the models, to understand instances such as high- and low-yield samples, to find possible explanations for erroneous predictions, and to identify regions impacted by particular stress. By employing advanced DL techniques along with an innovative approach to explainability, this study achieves highly accurate yield prediction while providing intuitive insights into the model’s decision-making process..