Enhancing Crop Yield Prediction Through Explainable AI for Interpretable Insights

The agriculture sector plays a pivotal role in sustaining global food security, necessitating advanced technologies to optimize crop yield prediction models. Crop yield depends on physiological, biochemical, and anatomical processes (Robert et al. in Introduction to the physiology of crop yield. Longman Group UK Limited., 1989 [1]). This paper introduces a novel approach that integrates explainable artificial intelligence techniques into the realm of crop yield prediction, aiming to provide interpretable and actionable insights for agricultural decision-makers. Leveraging machine learning algorithms, and historical crop data, our model not only forecasts future crop yields but also elucidates the factors contributing to the predictions in a transparent manner. The proposed methodology employs state-of-the-art XAI techniques such as LIME to unravel the decision-making process of the underlying predictive model. Informed decision-making gets aided by interpretability of the model and hence builds model trustability, essential for implementing intentional interventions required to resolve explicit challenges faced by varying regions and/or crops. It nurtures a collaborative ecosystem where stakeholders like farmers, subject matter experts, and agricultural policymakers get to actively partake in the process. Our work contributes to the field of agricultural technology by narrowing the gap between complex machine learning models and interpretable insights.