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dc.contributor.authorPhan, Viet Long-
dc.contributor.authorPham, van Dinh-
dc.contributor.otherLe, Thi Thu Nga-
dc.descriptionKỷ yếu Hội thảo Nghiên cứu khoa học của Sinh viên năm học 2021-2021; từ trang 19-23.vi_VN
dc.description.abstractShrimp farming plays an important role in aquaculture in the central coastal provinces of Vietnam. Shrimp disease is a significant threat to nutritional security and causes considerable economic loss. Identification of infected shrimps in aquaculture remains challenging due to the dearth of necessary infrastructure. The identification timely is an obligatory step to thwart from spread of disease. This paper proposes a technique to detect shrimp diseases based on transfer learning. This work includes three main steps. The first step collects and preprocess the image dataset of diseased shrimp collected from shrimp farms in Quang Nam province. The second step trains the dataset through three models SVM, VGG16, and the proposed model GonCNN. The third step tests and evaluates the accuracy of these models. Experimental results show that GonCNN has an accuracy of up to 92.93%, while SVM and VGG16 with 75.67% and 86.94% of accuracy, respectively.vi_VN
dc.publisherTrường Đại học Công nghệ Thông tin và Truyền thông Việt - Hànvi_VN
dc.subjectShrimp Diseasevi_VN
dc.subjectTransfer Learningvi_VN
dc.subjectDeep Learningvi_VN
dc.titleDiseased shrimp detection based on transfer learningvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:SV NCKH Năm học 2021-2022

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