Interpretable Machine Learning Techniques for an Advanced Crop Recommendation Model

Abstract

Achieving sustainable agricultural advancements necessitates optimizing crop yields while maintaining environmental stewardship. Our research addresses this critical imperative by introducing an innovative predictive model that refines crop recommendation systems through advanced machine learning techniques, specifically random forest and SHapley Additive exPlanations (SHAP). This study aims to overcome the limitations of traditional advisory approaches by incorporating interpretability tools, clarifying the model’s decision-making process around specific instances. To enhance the model’s local interpretability, we incorporated local interpretable model-agnostic explanations (LIMEs), providing transparent explanations for each crop recommendation, which fosters user trust, particularly when predictions diverge from established expert opinions. We conducted our empirical investigation using a comprehensive dataset that includes various agricultural parameters, historical crop yields, and environmental conditions to evaluate the model’s performance. Our findings indicate a significant improvement in predictive accuracy over traditional methods. The application of SHAP values offers a groundbreaking analysis of feature importance, enabling a precise quantification of the contributions of factors such as soil quality, climatic variables, and historical crop performance to the predictive outcomes. This research advances the field of precision agriculture by presenting a model that excels not only in accuracy but also in providing actionable insights through enhanced interpretability. By balancing advanced predictive capabilities with user-centric explanations, our model represents a substantial step forward in developing data-driven, transparent, and trustworthy agricultural advisories.