Fine-grained recognition of citrus varieties via wavelet channel attention network

Fine-grained visual classification (FGVC) has numerous applications in various sectors, including industrial, service, and agricultural sectors. However, the existing FGVC methods do not allow simultaneous capturing of local and global image features, leading to unsatisfactory results. To solve FGVC problems, this study proposes a wavelet channel attention network (WCANet). A WCANet improves multiscale feature extraction within channel attention modules by combining global average pooling (GAP) which captures global features—with wavelet transform (WT), which captures local features. Intelligent identification of citrus varieties is urgently required for the differentiated management of different varieties of citrus trees planted in a smart citrus orchard. However, no public dataset is currently available for use in the fine-grained recognition of citrus varieties for intelligent management of citrus orchards. Therefore, we developed a dataset, named citrus variety dataset (CVD), based on the canopy images of eight common citrus varieties. Experimental results show that when a WCANet is added to a 50-layer residual network (ResNet-50) and a 101-layer residual network (ResNet-101), the citrus variety identification accuracies of these two models are 96.67 % and 96.83 %, respectively, which are better than the corresponding accuracies of other channel attention modules with the same settings. Finally, by adding a WCANet to ResNet-50 and pretraining on ImageNet, a citrus variety identification accuracy of 99.10 % was achieved. In this study, we provide a performance enhancement solution for the expert systems used in the identification of citrus varieties and agricultural products.