Fish feeding behavior recognition model based on the fusion of visual and water quality features - Zheng ZHANG; Bosheng ZOU (Huanan Nongye Daxue xuebao)
ObjectiveTo improve the accuracy of fish feeding behavior recognition in industrial aquaculture environment. MethodA fish feeding behavior recognition model was proposed based on the fusion of visual and water quality features, namely MC-ConvNeXtV2. To better capture the global features of different aggregation levels and the detailed features of feeding behavior, a context-aware local attention mechanism (Cloatt) was introduced in each convolution stage of ConvNeXtV2-T. To improve the behavior recognition performance of the model in high-density aquaculture, a multimodal feature fusion module (MFFM) was designed to achieve adaptive fusion of visual features and dissolve oxygen, temperature, and pH of water quality features. The model test was conducted in a Micropterus salmoides culture factory with a culture density of 160 fish/m3. ResultThe test results showed that for the task of four feeding behaviors classification of fish school, the recognition accuracy, precision and recall of MC-ConvNeXtV2 model were 96.89%, 96.34%, and 96.59%, respectively. Compared with ConvNeXtV2-T, these indicators increased by 3.11, 2.42, and 2.72 percentage points, respectively. ConclusionThe proposed fish feeding behavior recognition model offers a new approach for intelligent aquaculture management.