Abstract

Routine monitoring of broiler chickens provides insights in the welfare status of a flock, helps to guarantee minimum defined levels of animal welfare and assists farmers in taking remedial measures at an early stage. Computer vision techniques offer exciting potential for routine and automated assessment of broiler welfare, providing an objective and biosecure alternative to the current more subjective and time-consuming methods. However, the current state-of-the-art computer vision solutions for assessing broiler welfare are not sufficient to allow the transition to fully automated monitoring in a commercial environment. Therefore, the aim of this study was to investigate the potential of computer vision algorithms for detection and resource use monitoring of broilers housed in both experimental and commercial settings, while also assessing the potential for scalability and resource-efficient implementation of such solutions. This study used a combination of detection and resource use monitoring methods, where broilers were first detected using Mask R-CNN and were then assigned to a specific resource zone using zone-based classifiers. Three detection models were proposed using different annotation datasets: model A with annotated broilers from a research facility, model B with annotated broilers from a commercial farm, and model A+B where annotations from both environments were combined. The algorithms developed for individual broiler detection performed well for both the research facility (model A, F-1 score > 0.99) and commercial farm (model A+B, F-1 score > 0.83) test data with an intersection over union of 0.75. The subsequent monitoring of resource use at the commercial farm using model A+B for broiler detection, also performed very well for the feeders, bale and perch (F-1 score > 0.93), but not for the drinkers (F-1 score = 0.28), which was likely caused by our evaluation method. Thus, the algorithms used in this study are a first step to measure resource use automatically in commercial application and allow detection of a large number of individual animals in a non-invasive manner. From location data of every frame, resource use can be calculated. Ultimately, the broiler detection and resource use monitoring might further be used to assess broiler welfare.

Keywords

broiler; computer vision; detection; resource use; animal welfare

Published in

Frontiers in animal science
2022, Volume: 3, article number: 945534
Publisher: FRONTIERS MEDIA SA

SLU Authors

• Guzhva, Oleksiy

  • Department of Biosystems and Technology, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation


UKÄ Subject classification

Computer Vision and Robotics (Autonomous Systems)


Publication identifier

DOI: https://doi.org/10.3389/fanim.2022.945534

Permanent link to this page (URI)

https://res.slu.se/id/publ/122881