Depth video data-enabled predictions of longitudinal dairy cow body
  weight using thresholding and Mask R-CNN algorithms
Ye Bi Leticia M. Campos Jin Wang Haipeng Yu Mark D. Hanigan Gota Morota
Monitoring cow body weight is crucial to support farm management decisions
due to its direct relationship with the growth, nutritional status, and health
of dairy cows. Cow body weight is a repeated trait, however, the majority of
previous body weight prediction research only used data collected at a single
point in time. Furthermore, the utility of deep learning-based segmentation for
body weight prediction using videos remains unanswered. Therefore, the
objectives of this study were to predict cow body weight from repeatedly
measured video data, to compare the performance of the thresholding and Mask
R-CNN deep learning approaches, to evaluate the predictive ability of body
weight regression models, and to promote open science in the animal science
community by releasing the source code for video-based body weight prediction.
A total of 40,405 depth images and depth map files were obtained from 10
lactating Holstein cows and 2 non-lactating Jersey cows. Three approaches were
investigated to segment the cow's body from the background, including single
thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived
biometric features, such as dorsal length, abdominal width, height, and volume,
were estimated from the segmented images. On average, the Mask-RCNN approach
combined with a linear mixed model resulted in the best prediction coefficient
of determination and mean absolute percentage error of 0.98 and 2.03%,
respectively, in the forecasting cross-validation. The Mask-RCNN approach was
also the best in the leave-three-cows-out cross-validation. The prediction
coefficients of determination and mean absolute percentage error of the
Mask-RCNN coupled with the linear mixed model were 0.90 and 4.70%,
respectively. Our results suggest that deep learning-based segmentation
improves the prediction performance of cow body weight from longitudinal depth
video data.