Dairy Cow rumination detection: A deep learning approach

TL;DR

This paper presents a deep learning-based system using CNNs to detect dairy cow rumination behavior from video data, achieving high accuracy and providing a non-invasive, efficient monitoring tool for livestock health.

Contribution

It introduces a novel CNN-based approach that classifies cow rumination using all postures in video frames, improving over previous methods that used limited postures.

Findings

Achieved 95% accuracy in rumination detection

Achieved 98% recall and precision

Demonstrated effectiveness of video-to-2D image conversion

Abstract

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.