A New Siamese Network Loss for Cattle Facial Recognition in a Few-Shot Learning Scenario
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Published:2024-08-20
Issue:3
Volume:6
Page:2941-2954
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ISSN:2624-7402
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Container-title:AgriEngineering
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language:en
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Short-container-title:AgriEngineering
Author:
Porto João12, Higa Gabriel1, Weber Vanessa23, Weber Fabrício2ORCID, Loebens Newton14ORCID, Claure Pietro3, de Almeida Leonardo5, Porto Karla2, Pistori Hemerson15ORCID
Affiliation:
1. Inovisão Department, Universidade Católica Dom Bosco, Campo Grande 79117-900, Brazil 2. Kerow Soluções de Precisão, Campo Grande 79117-900, Brazil 3. Faculty of Engineering, Universidade Estadual de Mato Grosso do Sul, Campo Grande 79115-898, Brazil 4. Faculty of Applied Mathematics, Universidade Federal do Pampa, Itaqui 97650-000, Brazil 5. Faculty of Engineering, Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, Brazil
Abstract
This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the proposed method aims to reduce the number of images needed, thereby minimizing animal stress. Systematic experiments conducted on datasets representing both full and few-shot learning scenarios revealed that the Siamese network consistently outperforms traditional models, such as ResNet101. It achieved notable improvements, with mean values increasing by over 6.5% and standard deviations decreasing by at least 0.010 compared to the ResNet101 baseline. These results highlight the Siamese network’s robustness and consistency, even in resource-constrained environments, and suggest that it offers a promising solution for enhancing model performance with fewer data and reduced animal stress, despite its slower training speed.
Funder
Dom Bosco Catholic University Foundation for the Support and Development of Education, Science and Technology from the State of Mato Grosso do Sul, FUNDECT Federal University of Pampa, UNIPAMPA Brazilian National Council of Technological and Scientific Development, CNPq Coordination for the Improvement of Higher Education Personnel, CAPES
Reference40 articles.
1. Das Indústrias Exportadoras de Carne (ABIEC) (2024, January 21). Perfil da Pecuária no Brasil. Associação Brasileira das Indústrias Exportadoras de Carne, Available online: https://www.abiec.com.br/. 2. Milanez, A.Y., Mancuso, R.V., Maia, G.B.d.S., Guimarães, D.D., Alves, C.E.A., and Madeira, R.F. (2024, January 21). Conectividade Rural: Situação Atual e Alternativas para Superação da Principal Barreira à Agricultura 4.0 No Brasil, Available online: https://web.bndes.gov.br/bib/jspui/handle/1408/20180. 3. Neto, A., Nicola, S., Moreira, J., and Fonte, B. (2021, January 16–18). Livestock Application: Naïve Bayes for Diseases Forecast in a Bovine Production Application: Use of Low Code. Proceedings of the International Conference on Innovations in Bio-Inspired Computing and Applications, Cham, Switzerland. 4. Identification and Recognition of Animals from Biometric Markers Using Computer Vision Approaches: A Review;Cihan;Kafkas Univ. Vet. Fak. Derg.,2023 5. Usage of few-shot learning and meta-learning in agriculture: A literature review;Dorsa;Smart Agric. Technol.,2023
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