A Hybrid Method for Identifying the Feeding Behavior of Tilapia

Abstract

Accurate feeding control in aquaculture using fish feeding behavior state is key to improving feed utilization efficiency and reducing water pollution. A hybrid method based on ResNet34-CBAM and migration learning is proposed in this paper to achieve tilapia feeding behavior classification. In order to enhance the diversity of samples, pre-processing, such as scaling and cropping, and adding noise, was performed on the images. An underwater camera was used for taking pictures of tilapia behavior, followed by feature extraction using a pre-trained ResNet34 residual network, training with an improved classification for identifying feeding behavior. The testing findings demonstrate that the suggested technique in this article is 99.72% accurate in an actual environment. The model also outperforms other recognition models based on MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5, and ResNet regarding accuracy, precision, and recall compared with other convolutional neural networks. Compared to the original model, the method shows a 7.84 percent improvement, indicating that it can correctly identify the feeding behavior of real fish in aquaculture and then scientifically determine the amount of baiting, which can improve feed utilization, increase aquaculture production, and decrease aquaculture costs.