Pose estimation-based lameness recognition in broiler using CNN-LSTM network

Abstract

Poultry behavior is a critical indicator of its health and welfare. Lameness is a clinical symptom indicating the existence of health problems in poultry. Therefore, lameness detection in the early stages is vital to broiler producers. In this study, a pose estimation-based model was developed to identify lameness in broilers through analyzing video footages for the first time. A deep convolutional neural network was used to detect and track seven key points on the bodies of walking broilers. Then consecutive extracted key points were fed into Long Short-Term Memory (LSTM) model to classify broilers according to a 6-point assessment method. This paper proposes the first large-scale benchmark for broiler pose estimation, consisting of 9,412 images. In addition, the dataset includes 400 videos (36,120 frames in total) of broilers with different gait score levels. The developed LSTM model achieved an overall classification accuracy of 95%, and the average per class classification accuracy was 97.5%. The obtained results prove that the pose estimation-based model as an automatic and non-invasive tool of lameness assessment can be applied to poultry farms for efficient management.