Artificial intelligence–based method for the rapid detection of fish parasites (Ichthyophthirius multifiliis, Gyrodactylus kobayashii, and Argulus japonicus)

• Published in: Aquaculture Vol. 563; p. 738790
• Main Authors: Li, Jiadong, Lian, Zirui, Wu, Zhelin, Zeng, Lihua, Mu, Liangliang, Yuan, Ye, Bai, Hao, Guo, Zheng, Mai, Kangsen, Tu, Xiao, Ye, Jianmin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30.01.2023
• Subjects: algorithms; aquaculture; aquaculture industry; Argulus japonicus; data collection; Deep learning; Fish parasites; goldfish; Gyrodactylus; Ichthyophthirius multifiliis; Internet; praziquantel; prediction; Protozoa; Rapid diagnosis system; rapid methods; Deep learning; Rapid diagnosis system; Fish parasites
• ISSN: 0044-8486; 1873-5622
• DOI: 10.1016/j.aquaculture.2022.738790

Ichthyophthirius (Ichthyophthirius multifiliis), Monogenea (Gyrodactylus kobayashii) and fish lice (Argulus japonicus) are mainly infectious parasites, representative species of Protozoa, Platyhelminthes and Arthropoda, which cause serious economic losses in aquatic industry. In this research, a visual system that can rapidly detect and count these three kinds of parasites was realized based on a one-stage object detection deep learning algorithm YOLOv4 through python. Firstly, we made a dataset of parasites containing 27,930 images. Secondly, weights of the trained fish lice model were applied as the pre-training weights, and network (backbone indeed) frozen was also applied to obtaining a good performance predicting model with less time and higher accuracy, which showed that Transfer Learning could meet the training requirement for detecting these three fish parasites by using self-made data set. In addition, by comparison of different one-stage algorithms YOLOv4‑tiny, YOLOv3, et al., the best model with a total average accuracy (mAP) of 95.41% was achieved by the YOLOv4. Finally, this model could quickly detect and count mixed infected pictures with a speed of 0.13 s per image measured in GPU time. Further, a visual prediction and counting system equipped with the YOLOv4 was developed by using PyQt which is convenient for real-time video detection. A simple drug-giving system equipped with Praziquantel was also developed based on the thought of the Internet of Things in this study and after using a drug, the number of monogeneans infecting gold fish was reduced. At the same time, we modified YOLOv4 PANet by adding additional detection layers, which achieved greater performance of detecting smaller targets like Monogenea. Together, this Artificial intelligence–based method could realize the rapid detection and diagnosis of fish parasites in images and video. •I. multifilii, G. kobayashii and A. japonicus were suitable for AI research in fish parasite diagnosis.•Data augment and transfer learning were applicable for fish parasite-based deep learning research.•YOLOv4 and modified-YOLOv4 showed great performance in fish parasite detection with a high speed.•A remote drug-given system deliver could reduce the amount and density of parasites when over threshold.