An imaging system based on deep learning for monitoring the feeding behavior of dairy cows
Journal
2019 ASABE Annual International Meeting
Date Issued
2019
Author(s)
Abstract
Observation of animal behavior in dairy farms is necessary for precision livestock farming as well as to prevent ailments that may affect the herd. Heat stress in dairy farms has been reported to be a serious problem that leads to the drastic decline in milk production and fertility of dairy cows. In recent studies, wearable devices were used to monitor the behavior of individual dairy cows. However, such kinds of contact devices may affect the behavior of the dairy cows for several reasons. Therefore, designing a non-contact system that can monitor the behavior of dairy cows is recommended. In this work, an embedded imaging system that can monitor the feeding behavior of dairy cows was developed. The system includes cameras that were fixed in front of the feeding area to acquire cow face images. Cow face detection and recognition were performed on the acquired images using a deep convolution neural network (CNN) to record feeding behavior and identify individual cow faces. The cow face detector has an F1-score of 0.971 based on validation with a static image testing dataset. Meanwhile, the cow face recognition model was also validated with an average F1-score of 0.85 on 19 different cows. Finally, the predictions of feeding time were compared with the manual observation with the R2 0.7802 without the cows having lower recall from the recognition results. In the future, combining the feeding time and temperature and humidity index can be a health indicator of individual cows.
Subjects
Cow face detection; Embedded system; Heat stress; Precision livestock farming; Recognition
Type
conference paper