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

Gary A Atkinson,Christopher K Reynolds,Melvyn L Smith,Jon M Moorby,Alison H Kingston-Smith,David J Humphries,David K Leemans,Lyndon N Smith

doi:10.1038/s41598-020-74511-0

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.

Highlights

The digestive health of cows is one of the primary factors that determine their well-being and productivity
There are certainly no obvious mistakes in the ordering of the samples except perhaps that the fourth-lowest-ranked sample should probably have a higher score. This is a result of the sample splitting in two when dropped and would likely be addressed using a more repeatable drop process
This research has proven the potential for the use of computer vision in the assessment of cattle faeces consistency and large particle content

Summary

Introduction

The digestive health of cows is one of the primary factors that determine their well-being and productivity. 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. Accurate feeding of animals in the beef and dairy industries is important both for efficient production and to reduce the impact of cattle farming on the wider environment. Minimised release of pollutants such as ammonia and urea (arising from poor utilisation of protein in the rumen and the animal) to land and water These important societal and economic targets are directly related to the mitigation of climatic impact of livestock and can be facilitated via the development of tools to support sustainable livestock production s ystems[1]

Methods

Results

Conclusion