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Article

A New Way to Identify Mastitis in Cows Using Artificial Intelligence

by
Rodes Angelo Batista da Silva
1,
Héliton Pandorfi
1,*,
Filipe Rolim Cordeiro
2,
Rodrigo Gabriel Ferreira Soares
3,
Victor Wanderley Costa de Medeiros
3,
Gledson Luiz Pontes de Almeida
1,
José Antonio Delfino Barbosa Filho
4,
Gabriel Thales Barboza Marinho
1 and
Marcos Vinícius da Silva
1
1
Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil
2
Department of Computing, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil
3
Department of Statistics and Informatic, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil
4
Department of Agricultural Engineering, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil
*
Author to whom correspondence should be addressed.
AgriEngineering 2024, 6(4), 4220-4232; https://doi.org/10.3390/agriengineering6040237
Submission received: 29 July 2024 / Revised: 22 October 2024 / Accepted: 30 October 2024 / Published: 8 November 2024
(This article belongs to the Section Livestock Farming Technology)
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Abstract

Mastitis is a disease that is considered an obstacle in dairy farming. Some methods of diagnosing mastitis have been used effectively over the years, but with an associated relative cost that reduces the producer’s profit. In this context, this sector needs tools that offer an early, safe, and non-invasive diagnosis and that direct the producer to apply resources to confirm the clinical picture, minimizing the cost of monitoring the herd. The objective of this study was to develop a predictive methodology based on sequential knowledge transfer for the automatic detection of bovine subclinical mastitis using computer vision. The image bank used in this research consisted of 165 images, each with a resolution of 360 × 360 pixels, sourced from a database of 55 animals diagnosed with subclinical mastitis, all of which were not exhibiting clinical symptoms at the time of imaging. The images utilized in the sequential learning transfer were those of MammoTherm, which is used for the detection of breast cancer in women. The optimized model demonstrated the most optimal network performance, achieving 92.1% accuracy, in comparison to the model with manual search (86.1%). The proposed predictive methodologies, based on knowledge transfer, were effective in accurately classifying the images. This significantly enhanced the automatic detection of both healthy animals and those diagnosed with subclinical mastitis using thermal images of the udders of dairy cows.
Keywords: image analysis; dairy cattle; convolutional neural network; infrared thermography image analysis; dairy cattle; convolutional neural network; infrared thermography

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MDPI and ACS Style

Silva, R.A.B.d.; Pandorfi, H.; Cordeiro, F.R.; Soares, R.G.F.; Medeiros, V.W.C.d.; Almeida, G.L.P.d.; Barbosa Filho, J.A.D.; Marinho, G.T.B.; Silva, M.V.d. A New Way to Identify Mastitis in Cows Using Artificial Intelligence. AgriEngineering 2024, 6, 4220-4232. https://doi.org/10.3390/agriengineering6040237

AMA Style

Silva RABd, Pandorfi H, Cordeiro FR, Soares RGF, Medeiros VWCd, Almeida GLPd, Barbosa Filho JAD, Marinho GTB, Silva MVd. A New Way to Identify Mastitis in Cows Using Artificial Intelligence. AgriEngineering. 2024; 6(4):4220-4232. https://doi.org/10.3390/agriengineering6040237

Chicago/Turabian Style

Silva, Rodes Angelo Batista da, Héliton Pandorfi, Filipe Rolim Cordeiro, Rodrigo Gabriel Ferreira Soares, Victor Wanderley Costa de Medeiros, Gledson Luiz Pontes de Almeida, José Antonio Delfino Barbosa Filho, Gabriel Thales Barboza Marinho, and Marcos Vinícius da Silva. 2024. "A New Way to Identify Mastitis in Cows Using Artificial Intelligence" AgriEngineering 6, no. 4: 4220-4232. https://doi.org/10.3390/agriengineering6040237

APA Style

Silva, R. A. B. d., Pandorfi, H., Cordeiro, F. R., Soares, R. G. F., Medeiros, V. W. C. d., Almeida, G. L. P. d., Barbosa Filho, J. A. D., Marinho, G. T. B., & Silva, M. V. d. (2024). A New Way to Identify Mastitis in Cows Using Artificial Intelligence. AgriEngineering, 6(4), 4220-4232. https://doi.org/10.3390/agriengineering6040237

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