Rotten strawberry classification based on EfficientNet V2 algorithm fused with GCN and CA-Transformer

ObjectiveImproving the accuracy and efficiency of rotting strawberry classification using modern computer vision techniques and deep learning methods.MethodsA classification method for rotten strawberries based on EfficientNet V2 fusion with Graph Convolutional Network (GCN) and Channel-Attention Transformer (CA-Transformer) has been proposed. Firstly, a graph convolution branch was added to the baseline model, which updated feature representations by aggregating the surrounding information of nodes, better capturing the contextual information of nodes in the graph structure. Secondly, this study integrated the Transformer structure with attention into the backbone of the baseline model, replacing some convolution operations with this structure to achieve the fusion of global and local features, thereby better identifying the rottenness of strawberries. Finally, learning parameters were introduced on the basis of the traditional residual structure to achieve dynamic feature fusion.ResultsThe GC-EfficientNet V2 model improved the accuracy by 1.86% and the recall by 1.49% compared to the baseline model. Compared with Inception V3, ResNet50, VGGNet, Vision Transformer, and EfficientNet V2-m, the recognition accuracy of the model was improved by 0.93%, 2.08%, 2.79%, 3.26%, and 0.47%, respectively.ConclusionThis model can accurately classify rotten strawberries, providing some theoretical support for automatic strawberry sorting.