TY - JOUR
T1 - Leveraging latent representations for milk yield prediction and interpolation using deep learning
AU - Liseune, Arno
AU - Salamone, Matthieu
AU - Van den Poel, Dirk
AU - Van Ranst, Bonifacius
AU - Hostens, Miel
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - Animal monitoring
KW - Autoencoder
KW - Convolutional neural network
KW - Deep learning
KW - Milk yield interpolation
KW - Milk yield prediction
UR - http://www.scopus.com/inward/record.url?scp=85087517717&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2020.105600
DO - 10.1016/j.compag.2020.105600
M3 - Article
AN - SCOPUS:85087517717
SN - 0168-1699
VL - 175
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 105600
ER -