A computer vision approach to improving cattle digestive health by the monitoring of faecal samples

Abstract

The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

“…2. As reported by Atkinson et al (2020), it is possible to detect the presence of undigested fibre and corn kernels (approximately 90%) using a deep learning approach. This indicates the importance of this system in monitoring the digestive health of ruminants, allowing for rapid intervention to change feed if digestibility issues arise.…”

Progress on Infrared Imaging Technology in Animal Production: A Review

“…In addition, technological advantages have become an essential factor driving the rapid development of animal husbandry [ 3 , 4 ]. Intelligent animal husbandry urgently needs new technical means to be used in animal production to achieve accurate, real-time, and dynamic monitoring of animal physiological states [ 5 , 6 , 7 , 8 ].…”

Deep learning image recognition of cow behavior and an open data set acquired near an automatic milking robot

“…Several technical solutions have been used to monitor animal welfare, including the use of digital video cameras (Porto et al 2015, Ardo et al 2017, Ter-Sarkisov et al 2017, depth sensor cameras (Nasirahmadi et al 2017), sound (Schirmann et al 2009);three-dimensional accelerometers (Müller et al 2003, Steeneveld and Hogeveen 2015, Gardenier et al 2018, Shen et al 2020; and infrared thermography (de Sousa et al 2018, Cuthbertson et al 2019, Xudong et al 2020, Anagnostopoulos et al 2021. Computer vision approaches employing video cameras are scalable and low-cost solutions (Banhazi and Tscharke 2016) and have been successfully used to monitor physiological and behavioral parameters related to pre-slaughter stress (Jorquera-Chavez et al 2019); to detect hoof disease (Gu et al 2017); to track gait and identify lameness (Gardenier et al 2018, Jiang et al 2019a, Kang et al 2022, to analyze health problems through calculating body condition scores (Zin et al 2018b, body structure b, Liu et al 2020) and faecal monitoring (Atkinson et al 2020); and to detect aggressive behaviors (Chen et al 2019). One often used computer vision method is to segment instances to recognize and track individual cows (Guzhva et al 2018, Ter-Sarkisov et al 2018, Zin et al 2018a, Qiao et al 2019, Shao et al 2019, Li et al 2021.…”