Automatic Newcastle disease detection using sound technology and deep learning method

Abstract

Newcastle disease (ND) is a common disease in poultry that has a great impact on poultry health and production. ND has destructive effects on the respiratory system, such as altering the acoustic features of bird vocalizations. For this reason, this research proposed a new method, the deep poultry vocalization network (DPVN), for the early detection of ND based on poultry vocalization. The method combined multiwindow spectral subtraction and high-pass filtering to reduce the influence of noise. In order to detect poultry vocalizations automatically, a multiple subband poultry vocalization endpoint detection method was proposed in this paper. The performance of the detection method was evaluated using the intersection-over-union (IOU) between the detected vocalizations and ground truth vocalizations. The recall of the detection method was 95.11%, and the precision was 96.54%. The audio features of poultry vocalizations are extracted by sound technology and used as the input of a deep learning network to recognize the vocalizations of poultry with Newcastle disease. Five different models were compared in the experiments. The method used in this paper achieves the best performance and the highest accuracy, recall and F1-score of 98.50%, 96.60% and 97.33%, respectively. The accuracies within the first, second, third and fourth days after infection were 82.15%, 90.00%, 93.60% and 98.50%, respectively. The experimental results show that the method proposed in this paper can be used to detect Newcastle disease in the early stage. It will be significant for improving animal welfare and the automated monitoring of poultry production.