Designing the Yellow Head Virus Syndrome Recognition Application for Shrimp on an Embedded System

Abstract

One of the most serious problems confronted by the shrimp farming industry is the disease caused by the yellow head virus (YHV). This research proposes an image processing algorithm to detect, identify and eliminate shrimp with the yellow head virus from the Litopenaeus vannamei gathering lines. Using a Raspberry Pi 3 module with the support of the OpenCV library which may be associated with Niblack’s algorithm is primarily suitable for segmentation. First, the shrimp object was identified and separated from the background using the image segmentation technique and the boundary that surrounds the object. Then, identification of diseased shrimp was analysed based on colour threshold. In this study, the sample of shrimp disease group had the highest amount of ratio, with about 6% to 11%. Most of the samples without the disease had a ratio of 0%. The experimental results show that the system can identify and accurately determine the coordinates of shrimp with yellow head virus disease and send information to the shrimp classification system in the food industry.

Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks

“…The purpose of this work is neither to discuss existing research on shrimp diseases in aquaculture through the lens of biologists [8,9] nor the effects of diseases over shrimp industry from an ecological perspective [10].By the time we wrote a literature review, there is very little research on the classification or detection of shrimp diseases based on images. YHV recognition application was designed in [11]. They proposed an image processing algorithm based on the Niblack algorithm to detect and eliminate shrimp with only YHV from the gather lines.…”

Using the Term Frequency-Inverse Document Frequency for the Problem of Identifying Shrimp Diseases with State Description Text

“…Firstly, diseases can be detected by biochemical techniques performed in the laboratory with measures such as CRISPR to detect white spot virus, acute hepatopancreatic necrosis disease (AHPND) [7], [8], amplification of recombinase polymerase to detect acute hepatopancreas, hemocyte iridescent virus [9], [10], PCR method to detect AHPND [11], etc. There is also an advance in shrimp disease identification methods by applying artificial intelligence algorithms on shrimp disease images, such as: using CNN architecture to create ShrimpNet [12]- [14] to detect yellow head disease in shrimp. This shows that the result of detection of diseases in shrimp is quite impressive, but there are limitations in terms of time and precision.…”