KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity

Simple Summary The diversity of fish resources is an important component of biodiversity. As a branch of fish, the diversity of koi varieties is conducive to improving the genetic quality of offspring, avoiding inbreeding, and improving their adaptability to the natural environment. The variety classification of koi is a necessary step to improve the diversity of koi varieties and breeding quality. The traditional manual classification method of koi variety faces some problems, such as high subjectivity, low efficiency, and high misclassification rate. Therefore, we studied an intelligent method of classifying koi variety using an artificial intelligence approach, and designed a deep learning network model, KRS-Net. The intelligent and nondestructive classification was realized for 13 varieties of koi by using the proposed model, and the accuracy rate was 97.9%, which is higher than that of the classical mainstream classification network. This study provides a reference for intelligent classification of marine organisms, and can be extended to the screening and breeding of other species. As the traditional manual classification method has some shortcomings, including high subjectivity, low efficiency, and high misclassification rate, we studied an approach for classifying koi varieties. The main contributions of this study are twofold: (1) a dataset was established for thirteen kinds of koi; (2) a classification problem with high similarity was designed for underwater animals, and a KRS-Net classification network was constructed based on deep learning, which could solve the problem of low accuracy for some varieties that are highly similar. The test experiment of KRS-Net was carried out on the established dataset, and the results were compared with those of five mainstream classification networks (AlexNet, VGG16, GoogLeNet, ResNet101, and DenseNet201). The experimental results showed that the classification test accuracy of KRS-Net reached 97.90% for koi, which is better than those of the comparison networks. The main advantages of the proposed approach include reduced number of parameters and improved accuracy. This study provides an effective approach for the intelligent classification of koi, and it has guiding significance for the classification of other organisms with high similarity among classes. The proposed approach can be applied to some other tasks, such as screening, breeding, and grade sorting.