Mastitis Classification in Dairy Cows Using Weakly Supervised Representation Learning

Soo-Hyun Cho, Mingyung Lee, Wang-Hee Lee, Seongwon Seo () and Dae-Hyun Lee ()
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Soo-Hyun Cho: Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Mingyung Lee: Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea
Wang-Hee Lee: Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Seongwon Seo: Division of Animal and Dairy Sciences, Chungnam National University, Daejeon 34134, Republic of Korea
Dae-Hyun Lee: Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea

Agriculture, 2024, vol. 14, issue 11, 1-17

Abstract: Detecting mastitis on time in dairy cows is crucial for maintaining milk production and preventing significant economic losses, and machine learning has recently gained significant attention as a promising solution to address this issue. Most studies have detected mastitis on time series data using a supervised learning model, which requires the scale of labeled data; however, annotating the onset of mastitis in milking data from dairy cows is very difficult and costly, while supervised learning relies on accurate labels for ensuring the performance. Therefore, this study proposed a mastitis classification based on weakly supervised representation learning using an autoencoder on time series milking data, which allows for concurrent milking representation learning and weakly supervision with low-cost labels. The proposed method employed a structure where the classifier branches from the latent space of a 1D-convolutional autoencoder, enabling representation learning of milking data to be conducted from the perspective of reconstructing the original information and detecting mastitis. The branched classifier backpropagate the mastitis symptoms, which are less costly than mastitis diagnosis, during the encoder’s representation learning. The results showed that the proposed method achieved an F1-score of 0.6 that demonstrates performance comparable to previous studies despite using low-cost labels. Our method has the advantage of being easily reproducible across various data domains through low-cost annotation for supervised learning and is practical as it can be implemented with just milking data and weak labels, which can be collected in the field.

Keywords: dairy cow; mastitis; representation learning; autoencoder; weakly supervised learning; anomaly detection; 1D-CNN (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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