Fish Type and Disease Classification Using Deep Learning Model Based Customized CNN with Resnet 50 Technique.

Autor:	Dash, Sambit, Ojha, Satyaswarup, Muduli, Raman Kumar, Patra, Saideep Priyadarshan, Barik, Ram Chandra
Předmět:	DEEP learning FISH diseases NOSOLOGY CONVOLUTIONAL neural networks HAZARDOUS wastes FISH farming
Zdroj:	Journal of Advanced Zoology; 2024, Vol. 45 Issue 3, p32-43, 12p
Abstrakt:	Aquaculture is a critical source of seafood production, addressing the global demand for fish products. Suggesting a Deep learning-based classification technique for fishes specifically Indian Major Carp (IMC) as Mrigala, Catla and Rohu is the major objective of this paper along with detecting the disease among them. This world inside hydrosphere has their own discrete living manner. Yet they are not untouched by diseases; fishes mostly affected when young carry pathogens which cause various infections naturally or due to environmental pollutants including chemical and hazardous waste. This paper proposed the classification and prediction of diseases of fishes in aquaculture using Deep Learning based customized Convolutional Neural Network with ResNet-50 model. The proposed model performance metric compared with recent state-of-art techniques. ResNet-50 classifies accurately the IMC and type of disease the fishes are suffering from. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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