AI-Based Computer Vision System For Intelligent Rice Quality Classification Using Deep Learning And XAI

Rice quality assessment plays a crucial role in the food industry as it directly affects consumer satisfaction, market value, and food safety. Traditional rice inspection methods rely mainly on manual observation and mechanical tools, which are time-consuming, labour-intensive, and prone to human error. To address these limitations, this study proposes an intelligent computer vision framework for automated rice quality assessment using deep learning and explainable artificial intelligence techniques. The system captures high-resolution images of rice grains and applies image preprocessing techniques such as grayscale conversion, edge detection, and segmentation to extract important visual features. Deep learning models, including VGG16 and ResNet50, are used to learn complex feature representations and classify rice grains based on their physical attributes such as size, shape, texture, and colour. To improve transparency and interpretability of the model predictions, Explainable AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) are integrated into the framework. Experimental results demonstrate that the proposed approach significantly improves classification accuracy and reliability compared to traditional inspection methods. The developed system provides an efficient, scalable, and automated solution for rice quality evaluation in agricultural and food processing industries.