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Rice is an essential staple food worldwide that is important in promotinginternational trade, economic growth, and nutrition. Asian countries such asChina, India, Pakistan, Thailand, Vietnam, and Indonesia are notable for theirsignificant contribution to the cultivation and utilization of rice. Thesenations are also known for cultivating different rice grains, including shortand long grains. These sizes are further classified as basmati, jasmine, kainatsaila, ipsala, arborio, etc., catering to diverse culinary preferences andcultural traditions. For both local and international trade, inspecting andmaintaining the quality of rice grains to satisfy customers and preserve acountry's reputation is necessary. Manual quality check and classification isquite a laborious and time-consuming process. It is also highly prone tomistakes. Therefore, an automatic solution must be proposed for the effectiveand efficient classification of different varieties of rice grains. Thisresearch paper presents an automatic framework based on a convolutional neuralnetwork (CNN) for classifying different varieties of rice grains. We evaluatedthe proposed model based on performance metrics such as accuracy, recall,precision, and F1-Score. The CNN model underwent rigorous training andvalidation, achieving a remarkable accuracy rate and a perfect area under eachclass's Receiver Operating Characteristic (ROC) curve. The confusion matrixanalysis confirmed the model's effectiveness in distinguishing between thedifferent rice varieties, indicating minimal misclassifications. Additionally,the integration of explainability techniques such as LIME (Local InterpretableModel-agnostic Explanations) and SHAP (SHapley Additive exPlanations) providedvaluable insights into the model's decision-making process, revealing howspecific features of the rice grains influenced classification outcomes.

Exploring Convolutional Neural Networks for Rice Grain Classification: An Explainable AI Approach