A deep learning method for monitoring spatial distribution of cage-free hens

Abstract

The spatial distribution of laying hens in cage-free houses is an indicator of flock's health and welfare. While larger space allows chickens to perform more natural behaviors such as dustbathing, foraging, and perching in cage-free houses, an inherent challenge is evaluating chickens' locomotion and spatial distribution (e.g., real-time birds' number on perches or in nesting boxes). Manual inspection of hen's spatial distribution requires closer observation, which is labor intensive, time consuming, subject to human errors, and stress causing on birds. Therefore, an automated monitoring system is required to track the spatial distribution of hens for early detection of animal welfare and health concerns. In this study, a non–intrusive machine vision method was developed to monitor hens' spatial distribution automatically. An improved You Only Look Once version 5 (YOLOv5) method was developed and trained to test hens' distribution in research cage-free facilities (e.g., 200 hens per house). The spatial distribution of hens the system monitored includes perch zone, feeding zone, drinking zone, and nesting zone. The dataset contains a whole growth period of chickens from day 1 to day 252. About 3000 images were extracted randomly from recorded videos for model training, validation, and testing. About 2400 images were used for training and 600 images for testing, respectively. Results show that the accuracy of the new model were 87–94% for tracking distribution in different zones for different ages of hens/pullets. Birds' age affected the performance of the model as younger birds had smaller body size and were hard to be detected due to blackness or occultation by equipment. The performance of the model was 0.891 and 0.942 for baby chicks (≤10 days old) and older birds (> 10 days) in detecting perching behaviors; 0.874 and 0.932 in detecting feeding/drinking behaviors. Miss detection happened when the flock density was high (>18 birds/m2) and chicken body was occluded by other facilities (e.g., nest boxes, feeders, and perches). Further studies such as chicken behavior identification works in commercial housing system should be combined with the model to reach an automatic detection system.

Keywords

• Cage-free system
• Precision farming
• Spatial distribution
• Deep learning