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

•Inferring milk yields along the entire lactation curve by using deep learning.•Extracting a latent representation of a lactation curve with autoencoders.•Predicting and interpolating milk yields simultaneously.•Modeling the impact of health and reproduction events on the lactation curve. In this st...

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Published in:Computers and electronics in agriculture Vol. 175; p. 105600
Main Authors: Liseune, Arno, Salamone, Matthieu, Van den Poel, Dirk, Van Ranst, Bonifacius, Hostens, Miel
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.08.2020
Elsevier BV
Subjects:
agriculture
Animal monitoring
Autoencoder
Breastfeeding & lactation
Convolutional neural network
cows
Culling
Deep learning
electronics
herds
Interpolation
lactation
lactation curve
lactation number
Milk
milk yield
Milk yield interpolation
Milk yield prediction
Multilayer perceptrons
neural networks
Representations
reproduction
statistics
yield forecasting
Deep learning
Milk yield interpolation
Animal monitoring
Milk yield prediction
Autoencoder
Convolutional neural network
ISSN:0168-1699, 1872-7107
Online Access:Get full text
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Summary:•Inferring milk yields along the entire lactation curve by using deep learning.•Extracting a latent representation of a lactation curve with autoencoders.•Predicting and interpolating milk yields simultaneously.•Modeling the impact of health and reproduction events on the lactation curve. 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.
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ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2020.105600