Artificial Intelligence Technologies for Monitoring Poultry Pecking
ISSN 2158-9429
Volume 16, Issue 1 - June 2023
Editor: Linda Chalker-Scott
Abstract
US Egg production is transitioning from the conventional cage to the cage-free system. While the cage-free system allows birds to perform natural behaviors such as dustbathing, foraging, and perching, an inherent challenge is feather pecking, one of the primary welfare issues in commercial cage-free hen houses as that can seriously reduce the well-being of birds and cause economic losses for egg producers. The objectives of this study were to develop a machine vision method and test the performance of new models in tracking the pecking behaviors and damages in cage-fee hens, and improve the detection accuracy of the model. Two deep learning models compared in tracking feather pecking behaviors/damages of cage-fee laying hens. According to the performance based on a dataset of 2000 images, the deep learning models have reached 85% accuracy. The model provides a basis for developing a real-time automatic model for tracking pecking damages in commercial cage-free houses to protect the health and welfare of hundreds of millions laying hens.
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