Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning

Yangyang Guo; Samuel E Aggrey; Peng Wang; Adelumola Oladeinde; Lilong Chai

doi:10.3390/ani12233390

Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning

Animals (Basel). 2022 Dec 2;12(23):3390. doi: 10.3390/ani12233390.

Authors

Yangyang Guo^{1

2}, Samuel E Aggrey², Peng Wang³, Adelumola Oladeinde⁴, Lilong Chai²

Affiliations

¹ School of Internet, Anhui University, Hefei 230039, China.
² Department of Poultry Science, University of Georgia, Athens, GA 30602, USA.
³ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
⁴ U.S. National Poultry Research Center, USDA Agricultural Research Service, Athens, GA 30605, USA.

Abstract

Animal behavior monitoring allows the gathering of animal health information and living habits and is an important technical means in precision animal farming. To quickly and accurately identify the behavior of broilers at different days, we adopted different deep learning behavior recognition models. Firstly, the top-view images of broilers at 2, 9, 16 and 23 days were obtained. In each stage, 300 images of each of the four broilers behaviors (i.e., feeding, drinking, standing, and resting) were segmented, totaling 4800 images. After image augmentation processing, 10,200 images were generated for each day including 8000 training sets, 2000 validation sets, and 200 testing sets. Finally, the performance of different convolutional neural network models (CNN) in broiler behavior recognition at different days was analyzed. The results show that the overall performance of the DenseNet-264 network was the best, with the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 days old, respectively. In addition, the efficient channel attention was introduced into the DenseNet-264 network (ECA-DenseNet-264), and the results (accuracy rates: 85%, 95%, 92%, 89.5%) confirmed that the DenseNet-264 network was still the best overall. The research results demonstrate that it is feasible to apply deep learning technology to monitor the behavior of broilers at different days.

Keywords: DenseNet; animal welfare; behavior recognition; deep learning; poultry production.

Grants and funding

USDA-ARS cooperative grants for the University of Georgia (58-6040-6-030 and 58-6040-8-034). USDA-NIFA Hatch Project (GEO00895): Future Challenges in Animal Production Systems: Seeking Solutions through Focused Facilitation; Georgia Research Alliance—Venture Fund.