Chapter 6: Automatic detection of injuries in turkeys: dealing with the prerequisites for a consistent annotation assessment

N. Volkmann; J. Brünger; C. Zelenka; J. Stracke; B. Spindler; R. Koch; N. Kemper

Chapter 6: Automatic detection of injuries in turkeys: dealing with the prerequisites for a consistent annotation assessment

In: Practical Precision Livestock Farming

Authors:

N. Volkmann

N. Volkmann Institute for Animal Hygiene, Animal Welfare and Animal Behaviour, University of Veterinary Medicine Hannover, Foundation, Bischofsholer Damm 15, 30173 Hannover, Germany.
Science and Innovation for Sustainable Poultry Production (WING), University of Veterinary Medicine Hannover, Foundation, Heinestraβe 1, 49377 Vechta, Germany.

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J. Brünger

J. Brünger Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University Kiel, Hermann-Rodewald-Straβe 3, 24118 Kiel, Germany.

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C. Zelenka

C. Zelenka Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University Kiel, Hermann-Rodewald-Straβe 3, 24118 Kiel, Germany.

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J. Stracke

J. Stracke Institute for Animal Hygiene, Animal Welfare and Animal Behaviour, University of Veterinary Medicine Hannover, Foundation, Bischofsholer Damm 15, 30173 Hannover, Germany.

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B. Spindler

B. Spindler Institute for Animal Hygiene, Animal Welfare and Animal Behaviour, University of Veterinary Medicine Hannover, Foundation, Bischofsholer Damm 15, 30173 Hannover, Germany.

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R. Koch

R. Koch Department of Computer Science, Faculty of Engineering, Christian-Albrechts-University Kiel, Hermann-Rodewald-Straβe 3, 24118 Kiel, Germany.

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N. Kemper

N. Kemper Institute for Animal Hygiene, Animal Welfare and Animal Behaviour, University of Veterinary Medicine Hannover, Foundation, Bischofsholer Damm 15, 30173 Hannover, Germany.

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Type:: Chapter

Pages:: 103–117

DOI:: https://doi.org/10.3920/978-90-8686-934-3_6

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Dar Hadith al Hassania

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In fattening turkeys, injurious pecking is a major health and welfare issue. Abnormal pecking behaviour occurs particularly when bleeding injuries act as a trigger. Earliest detection of these injuries is required to start intervention to prevent larger outbreaks. Thus, the aim of the presented study was to develop a commercial system for automatic detection of injuries in turkeys. As a mandatory initial step in the system development, a neural network was trained. Here, first insights into the agreement between the network and human observers is presented. Fattening turkey hens were video-recorded, and the videos were sliced into individual images for further processing. In the first step, these images were added to a developed annotation software that enables humans to mark pecking injuries in the images. Based on these manually labelled data, a neural network was trained to detect and label injuries in newly recorded images. The agreement between the observers and the network was not acceptable (32%). Therefore, a second step was added, where the previously made annotations were verified by presenting them a second time to the human observers in a modified software. In this software, a pseudo-binary system was created, and the observer had to confirm a correctly classified detection (‘injury’ versus ‘no injury’) while being blinded to the initial decision originating from the first step. A total of 24,173 opinions were given by human observers with 18.5% labelled detections rated as ‘no injury’, while 81.5% were classified as ‘injury’. In 81.6% of all labelled injury detections, the observers agreed on the classification. With the creation of a pseudo-binary system, the agreement between the human observers was improved substantially. In a future step, the network will receive the unanimous results from this second step for further training to improve its performance.

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Practical Precision Livestock Farming

Hands-on experiences with PLF technologies in commercial and R&D settings

E-Book ISBN:: 9789086869343

Publisher:: Wageningen Academic

Print Publication Date:: 12 Sep 2022

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Chapter 6: Automatic detection of injuries in turkeys: dealing with the prerequisites for a consistent annotation assessment

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Hands-on experiences with PLF technologies in commercial and R&D settings

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