Abstract
Simple SummaryBody condition score (BCS) is an important work for feeding management and cow welfare on the farm. The aim of our study is to assess the BCS automatically and replace the traditional manual method. In this study, we firstly built a non-contact and no-stress platform with a network camera, which can monitor the BCS of dairy cow remotely, and the back-view images of the cows were collected and the data set labeled by veterinary experts was built. Secondly, the improved Sing Shot multi-box Detector (SSD) algorithm was introduced to assess the BCS of each image. Finally, the experiments were carried out and the results showed the improved SSD had advantages of higher detecting speed and smaller model size compared with the original SSD.Body condition scores (BCS) is an important parameter, which is in high correlation with the health status of a dairy cow, metabolic disorder and milk composition during the production period. To evaluate BCS, the traditional methods rely on veterinary experts or skilled staff to look at a cow and touch it. These methods have low efficiency especially on large-scale farms. Computer vision methods are widely used but there are some improvements to increase BCS accuracy. In this study, a low cost BCS evaluation method based on deep learning and machine vision is proposed. Firstly, the back-view images of the cows are captured by network cameras, resulting in 8972 images that constituted the sample data set. The camera is a common 2D camera, which is cheaper and easier to install compared with 3D cameras. Secondly, the key body parts such as tails, pins and rump in the images were labeled manually, the Sing Shot multi-box Detector (SSD) method was used to detect the tail and evaluate the BCS. Inspired by DenseNet and Inception-v4, a new SSD was introduced by changing the network connection method of the original SSD. Finally, the experiments show that the improved SSD method can achieve 98.46% classification accuracy and 89.63% location accuracy, and it has: (1) faster detection speed with 115 fps; (2) smaller model size with 23.1 MB compared to original SSD and YOLO-v3, these are significant advantages for reducing hardware costs.
Highlights
Body condition score (BCS) is often used as a critical measure of how effective feeding is on a farm
In order to verify this performance of the above models, classification accuracy (CA) is employed
For each BCS level, a conclusion can be drawn that the extreme BCS levels have a higher accuracy than the middle BCS levels in all the three algorithms
Summary
Body condition score (BCS) is often used as a critical measure of how effective feeding is on a farm. BCS is an important parameter, which describes the relative fatness or energy reserves of a cow, regardless of body weight and frame size [1]. By estimating BCS, it can quickly see if the cow is outside. Animals 2019, 9, 470 its optimal condition. Calving BCS is an important determinant of early-lactation dry matter intake, milk yield and disease incidence [2]. The sick cows in a state of abnormal BCS should be separated to make new feeding plan. BCS is considered as an indicator of the herd productivity for farm management [3]. BCS contributes to healthier, more productive cows and saves on feed costs
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