Li Daoliang, Li Xin, Wang Qi, Hao Yinfeng
National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, China.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Animals (Basel). 2022 Oct 26;12(21):2938. doi: 10.3390/ani12212938.
Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.
水产品作为蛋白质的重要来源，已引起生产者和消费者的广泛关注。精确的鱼类疾病预防和治疗不仅可以提供健康的鱼类蛋白质，还能带来生态和经济效益。然而，与植物和农作物的智能二维诊断不同，智能水产养殖诊断面临的最严峻挑战之一是其三维空间。近几十年来，专家系统已被应用于鱼类疾病诊断，可对某些水产养殖进行有限的诊断。然而，这种方法需要水产养殖专业人员和专家。此外，诊断速度和效率也受到限制。因此，开发一种新的快速、自动和实时诊断方法至关重要。图像处理和计算机视觉技术的集成能够智能地实现鱼类疾病的诊断。本研究全面回顾了图像处理技术和基于图像的鱼类疾病检测方法，并分析了每种诊断方法在不同环境中的优缺点。尽管人们普遍认为疾病诊断和病原体识别有多种方法，但在检测准确性和速度方面仍需一些改进。构建鱼类疾病的增强现实三维图像、标准和共享数据集、深度学习以及数据融合技术将有助于提高鱼类疾病诊断的准确性和速度。
