A recurrent network based on active learning for the assessment of fish feeding status

Abstract

The image-based fish feeding status analysis has the potential to bring the aquaculture industry a more intelligent and precise feeding process. However, most of this type of research currently is predicated on a fully supervised approach that not only requires experienced experts for sample labeling, but also the process appears to be time consuming and resource intensive. This makes it very difficult to obtain labeled samples. To solve this problem, we proposed a model method that combines the VGG16 network with active learning to reduce the number of labeled samples needed in the analysis of fish feeding status. The algorithm is composed of two modules: feature extraction classifier and information extractor. In addition, multiple training strategies such as Mish activation function and RAdam optimizer were used in the feature extraction classifier to improve the classification performance. Optimal selection of information entropy method was used to obtain the most informative samples. To evaluate the effectiveness of our method, we designed a fish feeding experiment and verified the performance of the proposed algorithm with a special focus on the accuracy and the number of labeled samples required. The results showed that the proposed algorithm has achieved satisfying results in accuracy (the test accuracy is 98%) and the number of labeled samples (approximately 1/10 of the original dataset). Therefore, the method proposed in this paper can be applied to aquaculture production to provide a reference for farmers' feeding operations.