  Cattle activity is an essential index for monitoring health and welfare of
the ruminants. Thus, changes in the livestock behavior are a critical indicator
for early detection and prevention of several diseases. Rumination behavior is
a significant variable for tracking the development and yield of animal
husbandry. Therefore, various monitoring methods and measurement equipment have
been used to assess cattle behavior. However, these modern attached devices are
invasive, stressful and uncomfortable for the cattle and can influence
negatively the welfare and diurnal behavior of the animal. Multiple research
efforts addressed the problem of rumination detection by adopting new methods
by relying on visual features. However, they only use few postures of the dairy
cow to recognize the rumination or feeding behavior. In this study, we
introduce an innovative monitoring method using Convolution Neural Network
(CNN)-based deep learning models. The classification process is conducted under
two main labels: ruminating and other, using all cow postures captured by the
monitoring camera. Our proposed system is simple and easy-to-use which is able
to capture long-term dynamics using a compacted representation of a video in a
single 2D image. This method proved efficiency in recognizing the rumination
behavior with 95%, 98% and 98% of average accuracy, recall and precision,
respectively.
