Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning

Abstract

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained mod-els can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

“…In the agricultural and animal sciences, uptake of deep-learning techniques has been slow (Howard, 2018). Recently, however, our group applied a deep CNN to MIR spectra-matched pregnancy data and discovered such algorithms significantly improved the prediction accuracy for pregnancy status in dairy cows, a binary phenotype (Brand et al, 2018 Deep-learning tasks are known to require large volumes of data to successfully train a network. Moreover, for supervised learning problems, such as in the present study, there is an additional requirement that data labels must be more or less equally distributed (LeCun et al, 2015;Goodfellow et al, 2016).…”

“…We observed that milk MIR spectra contained features relating to pregnancy status and underlying metabolic changes in dairy cows, and that such features can be identified using a deep-learning approach. In our study, we defined pregnancy status as a binary trait (i.e., pregnant, not-pregnant) and found CNN significantly improved prediction accuracy, with trained models able to detect 83 and 73% of onsets and losses of pregnancy, respectively (Brand et al, 2018). More recently we have improved prediction accuracy such that models predict pregnancy status with an accuracy of 97% (with a corresponding validation loss of 0.08) after training for 200 epochs (our unpublished data).…”

“…Recently our group applied a deep convolutional neural network (CNN) to MIR-matched pregnancy data to predict the pregnancy status of dairy cows (Brand et al, 2018). We observed that milk MIR spectra contained features relating to pregnancy status and underlying metabolic changes in dairy cows, and that such features can be identified using a deep-learning approach.…”

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

“…Pregnancy results in changes to metabolism and energy requirements and leads to a repartitioning of resources to different physiological functions, compared with a nonpregnant lactating animal, and has a consequent influence on milk composition in dairy cattle, particularly in mid to late lactation (Olori et al, 1997;Loker et al, 2009;Penasa et al, 2016). Previous studies have examined the effect of pregnancy stage on detailed milk composition as determined by FT-MIR spectra (Lainé et al, 2017), and have reported the ability to predict conception outcomes (Hempstalk et al, 2015) or pregnancy (Toledo-Alvarado et al, 2018;Delhez et al, 2020;Brand et al, 2021) from FT-MIR spectra. Improvements in accuracy from incorporating FT-MIR data into pregnancy prediction models vary between studies.…”

Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods

“…Machine learning methods have been widely used in similar settings [13]. Examples include: detection of pregnancy status [14], bovine tuberculosis status [15], milk quality traits in dairy farms [16] and the risk of developing metritis, hyperketonemia and mastitis after calving by using the prepartum behaviour [17]. These methods can discover complex, latent patterns between predictor variables and the trait of interest-the disease status, in our work-even if this relationship is non-linear.…”