An improved strategy of wheat kernel recognition based on deep learning

Abstract

The detection of unsound wheat kernels in traditional wheat purchasing is affected by human factors, resulting in wrong wheat grading. At present, computer-based recognition of wheat kernels has generally low accuracy, and few types of wheat kernels can be recognized. To quickly, accurately, and objectively recognize wheat kernels, this study proposed an improved strategy of wheat kernel recognition method based on deep learning. First, a large number of collected wheat images were labeled, and the wheat kernels were divided into five categories: perfect kernels, broken kernels, impurities, sprouted kernels, and moldy kernels. Second, the improved strategies of VggNet-16, ResNet-34, EfficientNetb2, DenseNet121, and Vit models were proposed. Based on the two-stage target detection method, the improved network model was used to detect wheat kernels. Moreover, the accuracy of the model was verified by performing comparative tests. Results show that the improved network structure is obviously improved, and the highest accuracy rate of wheat kernel identification is 96%. The precision, recall rate, and F1-score of VggNet-16-W, ResNet-34-W, EfficientNet-b2-W, and DenseNet121-W models are above 97%. This study provides a good reference for rapid and accurate detection of wheat quality.