Fusion of machine learning and explainable AI for enhanced rice classification: a case study on Cammeo and Osmancik species

Abstract

The accurate identification and classification of rice species is critical for increasing crop productivity, quality, and diversity. Traditional rice classification methods involving manual inspection can be time-consuming, costly, and error-prone. This study addresses to address this challenge by exploring the potential of machine learning (ML) models for automated and accurate rice classification. The key objectives of this paper are threefold. First, the study evaluates the discriminative power of various morphological features extracted from rice grain images using feature selection methods. Second, it compares the performance of several ML models, including Artificial Neural Network (ANN), Categorical Boosting (CatBoost), Gradient Boosting (GBoost), k-Nearest Neighbours (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), in classifying two rice species (Cammeo and Osmancik). Third, the study implements explainable artificial intelligence (XAI) techniques, namely SHapley Additive exPlanation (SHAP) and Individual Conditional Expectation (ICE) plots, to provide transparency and interpretability into the inner workings and decision-making processes of the ML models. The findings indicate that the LR model achieved the highest classification accuracy, with a rate of 93.1%. Feature analysis identified Major Axis Length, Perimeter, Convex Area, and Area as the most influential features in distinguishing between rice species. This study highlights the successful application of advanced ML techniques in automating industrial rice classification, facilitating automated packaging and quality control processes without the need for human intervention. By improving the efficiency of rice classification and reducing reliance on manual labour, this approach offers significant benefits to both the agricultural industry and food production sectors.