Abstract
Rice is an important part of the food supply, its different varieties in terms of quality, flavor, nutritional value, and other aspects of the differences, directly affect the subsequent yield and economic benefits. However, traditional rice identification methods are time-consuming, inefficient, and prone to damage. For this reason, this study proposes a deep learning-based method to classify and identify rice with different flavors in a fast and non-destructive way. In this experiment, 19 categories of japonica rice seeds were selected, and a total of 36735 images were finally obtained. The lightweight network High Precision FasterNet (HPFasterNet) proposed in this study combines the Ghost bottleneck and FasterNet_T0 and introduces group convolution to compare the model performance. The results show that HPFasterNet has the highest classification accuracy of 92%, which is 5.22% better than the original model FasterNet_T0, and the number of parameters and computation is significantly reduced compared to the original model, which is more suitable for resource-limited environments. Comparison with three classical models and three lightweight models shows that HPFasterNet exhibits a more comprehensive and integrated performance. Meanwhile, in this study, HPFasterNet was used to test rice with different flavors, and the accuracy reached 98.98%. The experimental results show that the network model proposed in this study can be used to provide auxiliary experiments for rice breeding and can also be applied to consumer and food industries.