An automated computer vision based preliminary study for the identification of a heavy metal (Hg) exposed fish-channa punctatus

Issac, Ashish; Srivastava, Arti; Srivastava, Ashutosh; Dutta, Malay Kishore

doi:10.1016/j.compbiomed.2019.103326

Cited by 11 publications

(1 citation statement)

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“…The highest recognition accuracy of 96.87% was achieved using random forest. Issac et al proposed an automated non-destructive image processing method for identifying visual changes to distinguish between controlled (untreated) and heavy-metal-exposed (treated) fish [ 104 ]. The method used computer vision to detect changes in the eyes of fish caused by heavy metals, and the experiment allowed for the rapid identification of contaminated fish.…”

Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Wang

et al. 2022

Animals

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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.

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Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%