College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin, China
Mingyan Wang
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Wenjun Liu
College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin, China
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Dachun Feng
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Hang Zhang
College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin, China
College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin, China
Longqin Xu
Guangzhou Key Laboratory of Agricultural Product Quality and Safety Traceability Information Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
In aquaculture, quantifying the real-time feeding behaviour of fish is essential for making feeding decisions. However, most existing methods for assessing fish appetite are inefficient and subjective. To address these issues, this study proposes an improved tilapia feeding level classification model with ResNet34. First, we introduce the attention module CBAM into the ResNet34 model to adjust the attention of the model according to the importance of different channel features and enhance the effective extraction of important features. We then used migration learning to transfer the knowledge learned from the source data (ImageNet dataset) to the tilapia ingestion image dataset, which allowed us to train the tilapia ingestion behaviour classification model faster while retaining the pre-trained model. Experimental results showed that the improved ResNet34 model in this study achieved an accuracy of 99.72%, an improvement of 7.84 percentage points over the original model. In addition, the model outperformed models such as MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5 and ResNet18 in terms of accuracy, precision, recall and F1 scores.These results suggest that the proposed method can accurately identify feeding behavior of fish and provide a scientific basis for determining feeding amounts.
