End-to-end detection of cough and snore based on ResNet18-TF for breeder laying hens: A field study

作者：Hongbo Ma, Peng Xin, Juncheng Ma, Yang Xiao, Ruohan Zhang, Chao Liang, Yu Liu, Fei Qi, Chaoyuan Wang · 发表于：Artificial Intelligence in Agriculture · 年份：2025 · DOI：10.1016/j.aiia.2025.11.002 · 被引用次数：3 · 研究领域：Animal Nutrition and Physiology、Animal Vocal Communication and Behavior、Food Supply Chain Traceability

Cough and snore are the most representative vocalizations for chicken respiratory diseases, which severely restrict poultry health due to highly contagious and lethal characteristics. Nighttime inspection by veterinarians is the foremost solution to identify bird respiratory symptoms during production. However, it is subjective, time-consuming, and labor-intensive. This study proposed a novel end-to-end model (ResNet18-TF) based on ResNet18 and a Time-Frequency Attention Mechanism (TFBlock) to automatically recognize chicken cough and snore using data collected in a commercial layer breeder house. In addition, a comparative analysis was conducted to evaluate the performance of different input features. The results revealed that LogFbank features exhibited superiority over MFCC features in the task of chicken sound recognition. By incorporating first-order and second-order delta features into LogFbank, the combination of ‘LogFbank+ΔLogFbank+ΔΔLogFbank’ further improved model recognition accuracy by 2.34 %. Additionally, the TFBlock structure enhanced the model's performance for recognizing coughs and snores. Specifically, the F1-score of MobileViTv3-TF, EfficientNetV2-TF, and ResNet18-TF models were increased by 1.30 %, 0.88 %, and 1.84 %, respectively, compared to their respective counterparts without TFBlock. ResNet18-TF achieved the highest accuracy, precision, recall, and F1-score, with 94.37 %, 94.59 %, 94.56 %, and 94.57 %, respectively. The generalization of ResNet18-TF...