Dried shiitake mushroom grade recognition using D-VGG network and machine vision

作者：Li Wang, Penghao Dong, Qiao Wang, Kunming Jia, Qunfeng Niu · 发表于：Frontiers in Nutrition · 年份：2023 · DOI：10.3389/fnut.2023.1247075 · 被引用次数：9 · 研究领域：Smart Agriculture and AI、Spectroscopy and Chemometric Analyses、Fungal Biology and Applications

Grading dried shiitake mushrooms is an indispensable production step, as there are large quality differences between different grades, which affect the product's price and marketability. Dried shiitake mushroom samples have irregular shapes, small morphological differences between different grades of the same species, and they may occur in mixed grades, which causes challenges to the automatic grade recognition using machine vision. In this study, a comprehensive method to solve this problem is provided, including image acquisition, preprocessing, dataset creation, and grade recognition. The osprey optimization algorithm (OOA) is used to improve the computational efficiency of Otsu's threshold binarization and obtain complete mushroom contours samples efficiently. Then, a method for dried shiitake mushroom grade recognition based on the improved VGG network (D-VGG) is proposed. The method uses the VGG16 network as the base framework, optimizes the convolutional layer of the network, and uses a global average pooling layer instead of a fully connected layer to reduce the risk of model overfitting. In addition, a residual module and batch normalization are introduced to enhance the learning effect of texture details, accelerate the convergence of the model, and improve the stability of the training process. An improved channel attention network is proposed to enhance the feature weights of different channels and improve the grading performance of the model. The experimental res...