Leveraging latent representations for milk yield prediction and interpolation using deep learning

Arno Liseune*, Matthieu Salamone, Dirk Van den Poel, Bonifacius Van Ranst, Miel Hostens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this study, we propose a lactation model that estimates the daily milk yield by using autoencoders to generate a latent representation of all milk yields observed during the entire lactation cycle, irrespective of the length of the time interval between the different measurements. More specifically, we propose a sequential autoencoder (SAE) to process the sequential data, extract and decode the low-dimensional representations and generate the milk yield sequences. The SAE is compared with a more traditional multilayer perceptron model (MLP) which uses herd and parity information and lagged milk yields as input. Results show that incorporating the recorded daily milk yields, lactation number, herd statistics as well as reproduction and health events the cow encountered during the lactation cycle results in the most qualitative latent representations. Moreover, by leveraging these low-dimensional encodings, the SAE reconstructed the entire milk yield curve with a higher accuracy than the MLP. Hence, we present a framework that is able to infer missing milk yields along the entire lactation curve which facilitates selection and culling decisions as well as the estimation of future earnings and costs. Furthermore, the model allows farmers to enhance their animal monitoring systems as it incorporates the sequence of health and reproduction events to forecast the cow's future productivity.

Original languageEnglish
Article number105600
Number of pages11
JournalComputers and Electronics in Agriculture
Volume175
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes

Keywords

  • Animal monitoring
  • Autoencoder
  • Convolutional neural network
  • Deep learning
  • Milk yield interpolation
  • Milk yield prediction

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