Rice Grains Detection, Classification, and Quality Prediction Using Deep Learning

Abstract: Rice, as the most consumed food worldwide, faces a continual demand, necessitating rigorous quality inspection for both local consumption and international trade. Manual quality assessment methods are fraught with issues such as time consumption, high costs, and error susceptibility. This research paper introduces an innovative solution employing Deep Convolutional Neural Networks (CNNs) to automate rice grain detection, classification, and quality prediction from scanned images. The methodology integrates comprehensive image pre-processing and quality assessment techniques, encompassing image acquisition, pre-processing, CNN architecture construction, model training, and evaluation. Notably, it includes noise removal and segmentation to optimize input images. The grain samples undergo image processing using MATLAB, where an algorithm is applied to analyze their features. Classification is then performed based on the color, shape, and size characteristics of the grains. Various image features are extracted such as centroids, mean intensity, chalkiness, and perimeter further aid in quality assessment. Performance evaluation of the trained model is conducted through confusion matrix analysis and computation of classification metrics like accuracy, specificity, sensitivity, precision, recall, and F1-score. This methodology offers a robust framework for automated rice grain classification and quality prediction, thereby significantly enhancing efficiency and reliability in the global rice industry