Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning

Artigo Acesso aberto Revisado por pares

Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning

2024; Multidisciplinary Digital Publishing Institute; Volume: 14; Issue: 13 Linguagem: Inglês

10.3390/ani14131850

ISSN

2076-2615

Autores

Fernando Joaquin Ramírez-Coronel, Oscar M. Rodríguez-Elias, Edgard Esquer-Miranda, Madaín Pérez‐Patricio, Anna Judith Pérez‐Báez, Eduardo A. Hinojosa-Palafox,

Tópico(s)

Smart Agriculture and AI

Resumo

Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (

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Non-Invasive Fish Biometrics for Enhancing Precision and Understanding of Aquaculture Farming through Statistical Morphology Analysis and Machine Learning