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KRS-Net: A Classification Approach Based on Deep Learning for Koi with High Similarity
Youliang Zheng1, Limiao Deng2, Qi Lin3
1College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China.
Biology
|December 23, 2022
Summary
A new deep learning network, KRS-Net, accurately classifies koi varieties, overcoming limitations of manual methods. This intelligent approach achieves 97.90% accuracy for koi classification, aiding breeding and sorting.
Area of Science:
- * Aquaculture and computer vision
- * Machine learning for biological classification
Background:
- * Traditional manual koi classification suffers from subjectivity, inefficiency, and high error rates.
- * Distinguishing between highly similar koi varieties presents a significant challenge in underwater animal classification.
Purpose of the Study:
- * To develop an automated and accurate method for classifying thirteen koi varieties.
- * To address the limitations of existing classification techniques for visually similar aquatic organisms.
Main Methods:
- * Creation of a dedicated dataset for thirteen koi varieties.
- * Design and implementation of the KRS-Net deep learning classification network.
- * Comparative analysis against established networks: AlexNet, VGG16, GoogLeNet, ResNet101, and DenseNet201.
Main Results:
- * KRS-Net achieved a classification accuracy of 97.90% on the established koi dataset.
- * The proposed network outperformed five mainstream classification networks in accuracy.
- * KRS-Net demonstrated a reduced number of parameters compared to other models.
Conclusions:
- * KRS-Net offers an effective and intelligent solution for automated koi variety classification.
- * The approach shows promise for classifying other aquatic organisms with high inter-class similarity.
- * Applications include screening, breeding programs, and grade sorting in aquaculture.
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