A New Way to Identify Mastitis in Cows Using Artificial Intelligence

Rodes Angelo Batista da Silva; 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; Marcos Vinícius da Silva

doi:10.3390/agriengineering6040237

journal article Open Access Nov 08, 2024

A New Way to Identify Mastitis in Cows Using Artificial Intelligence

Rodes Angelo Batista da Silva 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

Marcos Vinícius da Silva

AgriEngineering Vol. 6 No. 4 pp. 4220-4232 · MDPI AG

View at Publisher Save 10.3390/agriengineering6040237

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.

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References

38

[1]

Zaninelli, M., Redaelli, V., Luzi, F., Bronzo, V., Mitchell, M., Dell’Orto, V., Bontempo, V., Cattaneo, D., and Savoini, G. (2018). First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows. Sensors, 18. 10.3390/s18030862

[2]

Zhang "Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector" Comput. Electron. Agric. (2020) 10.1016/j.compag.2020.105754

[3]

Pandorfi "Thermal Image Thresholding for Automatic Detection of Bovine Mastitis" Int. J. Comput. Appl. (2021)

[4]

Watz "Technical note: Automatic evaluation of infrared thermal images by computerized active shape modeling of bovine udders challenged with Escherichia coli" J. Dairy Sci. (2019) 10.3168/jds.2018-15761

[5]

Viguier "Mastitis detection: Current trends and future perspectives" Trends Biotechnol. (2009) 10.1016/j.tibtech.2009.05.004

[6]

A 100-Year Review: Mastitis detection, management, and prevention

Pamela L. Ruegg

Journal of Dairy Science 2017 10.3168/jds.2017-13023

[7]

Polat "Sensitivity and specificity of infrared thermography in detection of subclinical mastitis in dairy cows" J. Dairy Sci. (2010) 10.3168/jds.2009-2807

[8]

Pandorfi "Spatial dependence of udder surface temperature variation in dairy cows with healthy status and mastitis" Rev. Bras. De Saúde E Produção Anim. (2019) 10.1590/s1519-99402001102019

[9]

McManus, R., Boden, L.A., Weir, W., Viora, L., Barker, R., Kim, Y., McBride, P., and Yang, S. (2022). Thermography for disease detection in livestock: A scoping review. Front. Vet. Sci., 9. 10.3389/fvets.2022.965622

[10]

Yang "Udder skin surface temperature variation pre- and post- milking in dairy cows as determined by infrared thermography" J. Dairy Res. (2018) 10.1017/s0022029918000213

[11]

Bortolami "Evaluation of the udder health status in subclinical mastitis affected dairy cows through bacteriological culture, somatic cell count and thermographic imaging" Pol. J. Vet. Sci. (2015) 10.1515/pjvs-2015-0104

[12]

Pezeshki "Variation of inflammatory dynamics and mediators in primiparous cows after intramammary challenge with Escherichia coli" Vet. Res. (2011) 10.1186/1297-9716-42-15

[13]

Rodrigues "Identification of mammary lesions in thermographic images: Feature selection study using genetic algorithms and particle swarm optimization" Res. Biomed. Eng. (2019) 10.1007/s42600-019-00024-z

[14]

Berry "Daily variation in the udder surface temperature of dairy cows measured by infrared thermography: Potential for mastitis detection" Can. J. Anim. Sci. (2003) 10.4141/a03-012

[15]

Wilson "The marginal value of adaptive gradient methods in machine learning" Adv. Neural Inf. Process. Syst. (2017)

[16]

Kermany "Identifying medical diagnoses and treatable diseases by image-based deep learning" Cell (2018) 10.1016/j.cell.2018.02.010

[17]

Porter "Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle" J. Dairy Sci. (2021) 10.3168/jds.2020-19642

[18]

Boumaraf, S., Liu, X., Zheng, Z., Ma, X., and Ferkous, C. (2021). A new transfer learning-based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomed. Signal Process. Control, 63. 10.1016/j.bspc.2020.102192

[19]

Negassi "Application of artificial neural networks for automated analysis of cystoscopic images: A review of the status and prospects" World J. Urol. (2020) 10.1007/s00345-019-03059-0

[20]

Das "Impact of heat stress on health and performance of dairy animals: A review" Vet. World (2016) 10.14202/vetworld.2016.260-268

[21]

Brain tumor classification for MR images using transfer learning and fine-tuning

Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir et al.

Computerized Medical Imaging and Graphics 2019 10.1016/j.compmedimag.2019.05.001

[22]

ImageNet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher et al.

2009 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2009.5206848

[23]

Yang, L., and Shami, A. (2022). On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice. arXiv.

[24]

Bressem, K.K., Adams, L.C., Erxleben, C., Hamm, B., Niehues, S.M., and Vahldiek, J.L. (2020). Comparing different deep learning architectures for classification of chest radiographs. Sci. Rep., 10. 10.1038/s41598-020-70479-z

[25]

Pelikan "BOA: The Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference" Citeseer (1999)

[26]

Sameen "Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment" Catena (2020) 10.1016/j.catena.2019.104249

[27]

Yosinski "How transferable are features in deep neural networks?" Adv. Neural Inf. Process. Syst. (2014)

[28]

Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization

Malliga Subramanian, Narasimha Prasad L.V., Janakiramaiah B. et al.

Big Data 2022 10.1089/big.2021.0218

[29]

Siemon, M.S.N., Shihavuddin, A.S.M., and Ravn-Haren, G. (2021). Sequential transfer learning based on hierarchical clustering for improved performance in deep learning-based food segmentation. Sci. Rep., 11. 10.1038/s41598-020-79677-1

[30]

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Communications of the ACM 2017 10.1145/3065386

[31]

Bezerra "An empirical correlation to estimate thermal properties of the breast and of the breast nodule using thermographic images and optimization techniques" Int. J. Heat Mass Transf. (2020) 10.1016/j.ijheatmasstransfer.2019.119215

[32]

Gayathri "Udder thermogram-based deep learning approach for mastitis detection in Murrah buffaloes" Comput. Electron. Agric. (2024) 10.1016/j.compag.2024.108906

[33]

Brito "Sensitivity and specificity of the California Mastitis Test as a diagnostic tool for subclinical mastitis in quarter somatic cell count estimation" Braz. J. Vet. Res. (1997)

[34]

Macêdo, D., Dreyer, P., Ludermir, T., and Zanchettin, C. (2021). Training Aware Sigmoidal Optimizer. arXiv. 10.1007/978-3-031-21689-3_10

[35]

"Analysis of Methods of Classification of Breast Thermographic Images to Determine their Viability in the Early Breast Cancer Detection" IEEE Lat. Am. Trans. (2018) 10.1109/tla.2018.8444159

[36]

Abiwinanda "Brain Tumor Classification Using Convolutional Neural Network" Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018 (2019) 10.1007/978-981-10-9035-6_33

[37]

Mehrotra "A Transfer Learning approach for AI-based classification of brain tumors" Mach. Learn. Appl. (2020)

[38]

Martins "Mastitis detection in sheep by infrared thermography" Res. Vet. Sci. (2013) 10.1016/j.rvsc.2012.10.021

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References

Details

Published: Nov 08, 2024
Vol/Issue: 6(4)
Pages: 4220-4232
License: View

Authors

R

Rodes Angelo Batista da Silva

Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

H

Héliton Pandorfi

Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

F

Filipe Rolim Cordeiro

Department of Computing, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

R

Rodrigo Gabriel Ferreira Soares

Department of Statistics and Informatic, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

V

Victor Wanderley Costa de Medeiros

Department of Statistics and Informatic, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

G

Gledson Luiz Pontes de Almeida

Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

J

José Antonio Delfino Barbosa Filho

Department of Agricultural Engineering, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil

G

Gabriel Thales Barboza Marinho

Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

M

Marcos Vinícius da Silva

Department of Agricultural Engineering, Rural Federal University of Pernambuco, Recife 52171-900, PE, Brazil

Funding

Federal University of Pernambuco—UFRPE and the Foundation for the Support of Science and Technology of the State of Pernambuco Award: APQ-0496-5.03/22

Cite This Article

Rodes Angelo Batista da Silva, Héliton Pandorfi, Filipe Rolim Cordeiro, et al. (2024). A New Way to Identify Mastitis in Cows Using Artificial Intelligence. AgriEngineering, 6(4), 4220-4232. https://doi.org/10.3390/agriengineering6040237

A New Way to Identify Mastitis in Cows Using Artificial Intelligence

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