Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

Abstract

Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

“…Improvement in the digital image analysis especially for the segmentation of the myosepta could be a way to quantify more precisely muscle fat. Beside the classical segmentation techniques, the development of deep learning methods could allow a better segmentation on low contrast images ( Fernandes et al, 2020 ). However, these techniques require the training of a CNN (convolutional neural network) like U-Net ( Falk et al, 2019 ) with manually annotated images (ground truth).…”

“…To estimate lipid content and its distribution, X-ray computed tomography (CT) and magnetic resonance imaging (MRI) can be applied to a representative cutlet and the results evaluated using image analysis, an approach that has already been used for the phenotypic estimation of fat content in fish ( Gjerde and Martens, 1987 ; Kolstad et al, 2004 ; Marty-Mahé et al, 2004 ; Toussaint et al, 2005 ; Mathiassen et al, 2011 ; Collewet et al, 2013 ; Picaud et al, 2016 ). Digital imaging processing and the MRI are examples of computer vision systems ( Fernandes et al, 2020 ). Thanks to the visual contrast between fat and muscle, subcutaneous and intramuscular fat can also be measured in images acquired with a charge-coupled device camera ( Marty-Mahe et al, 2003 ) or with a desktop scanner ( Kause et al, 2008 ; Collewet et al, 2013 ).…”

Automatic Assessment of Keel Bone Damage in Laying Hens at the Slaughter Line

“…For an automated assessment, image processing techniques can be applied. They are a cheap, non-contact and cost-effective tool, and have already been used to monitor diverse livestock health and behavior measures (reviewed by [ 18 , 19 , 20 ]). Automatic, image-based assessment systems are routinely applied to, e.g., the evaluation of foot pad lesions in broilers and turkeys [ 21 , 22 , 23 ].…”

ASAS-NANP SYMPOSIUM: prospects for interactive and dynamic graphics in the era of data-rich animal science1

“…Recent data collection methods have spawned a new type of data structure where the original data are images ( Fernandes et al, 2020b ). Examples include the computed tomography scan of carcasses ( Navajas et al, 2010 ), thermal imaging ( Scoley et al, 2019 ), and depth images ( Kongsro, 2014 ) and more examples under the Computer vision section.…”