Explainable artificial intelligence and interpretable machine learning for agricultural data analysis

Abstract

Artificial intelligence and machine learning have been increasingly applied for prediction in agricultural science. However, many models are typically black boxes, meaning we cannot explain what the models learned from the data and the reasons behind predictions. To address this issue, I introduce an emerging subdomain of artificial intelligence, explainable artificial intelligence (XAI), and associated toolkits, interpretable machine learning. This study demonstrates the usefulness of several methods by applying them to an openly available dataset. The dataset includes the no-tillage effect on crop yield relative to conventional tillage and soil, climate, and management variables. Data analysis discovered that no-tillage management can increase maize crop yield where yield in conventional tillage is <5000 kg/ha and the maximum temperature is higher than 32°. These methods are useful to answer (i) which variables are important for prediction in regression/classification, (ii) which variable interactions are important for prediction, (iii) how important variables and their interactions are associated with the response variable, (iv) what are the reasons underlying a predicted value for a certain instance, and (v) whether different machine learning algorithms offer the same answer to these questions. I argue that the goodness of model fit is overly evaluated with model performance measures in the current practice, while these questions are unanswered. XAI and interpretable machine learning can enhance trust and explainability in AI.