Classifying rice grains using deep learning
Rice, one of the world’s most significant agricultural products, is crucial for human nutrition, economies, and various industrial sectors. Classifying rice varieties, an essential part of rice supply management is often time-consuming, energy-intensive, and expensive. With over 120,000 rice varieties categorized by the International Rice Research Institute based on milling degree, kernel size, starch content, and flavour, the need for automation in rice grain classification is evident. We evaluated the performance of various contemporary deep-learning models, including Residual Network (ResNet), Visual Geometry Group (VGG) network, EfficientNet, and MobileNet. These models were tested on a dataset comprising 75,000 images, classified into five different rice categories. We assessed each model using established evaluation metrics such as accuracy, F1 score, precision, recall, and per-class accuracy. Our findings showed that the EfficientNet-based model delivered the highest accuracy (99.67%), while the MobileNet-based model excelled in the speed of classification (2556 s). We concluded that, compared to traditional machine learning methods, the models employed in our study are highly scalable and capable of managing large volumes of complex data with millions of features and samples. The original article can be read online here.