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A lightweight deep learning architecture for automatic shrimp disease classification

Abstract Globally, shrimp aquaculture is a vital source of food, but disease outbreaks present serious economic challenges. Traditional diagnostic techniques, such as visual inspection and polymerase chain reaction (PCR), are limited in their ability to detect diseases in real-time because they are...

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Principais autores: Sandhya Sharma, Poltak Sandro Rumahorbo, Satoshi Kondo, Shinya Watanabe, Yoshifumi Okada, Bishnu Prasad Gautam, Kazuhiko Sato
Format: Artigo
Sprog:Inglês
Udgivet: Nature Portfolio 2026-03-01
Serier:Scientific Reports
Fag:
Shrimp disease classification
Desktop application
Lightweight neural network
Aquaculture technology
FeatherNetX
Online adgang:https://doi.org/10.1038/s41598-026-44195-z
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