Year: 2024
Type: article
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.
Source: Agriculture
Institution: Chungnam National University
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,955
Funders: Chungnam National University, Ministry of Agriculture, Food and Rural Affairs, Rural Development Administration, Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries, Ministry of Science and ICT, South Korea, Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry
OpenAlex ID: W4404525671