WCANET: WAVELET CHANNEL ATTENTION NETWORK FOR CITRUS VARIETY IDENTIFICATION

The effective fine-grained identification of citrus varieties plays a vital role in the differential production management of citrus orchards. To our knowledge, there are few studies and publicly available datasets on fine-grained identification of citrus varieties. In this study, we propose Wavelet Channel Attention Network (WCANet) to solve the problem of fine-grained visual classification of citrus varieties and create a Citrus Variety Dataset (CVD) consisting of tree canopy images. WCANet combines global average pooling to extract global features and wavelet transform to capture local features, which greatly improves the capability of channel attention modules for multi-scale feature extraction. Experimental results demonstrate that the WCANet outperforms the state-of-the-art confidence estimation approaches on various benchmarks. Our code and dataset will be open-sourced at https://github.com/fightero/WCANet.