Decoding laying hen behavior and physiological status through acoustic biomarkers: temporal patterns, rooster-hen vocalization identification in group housing and environmental adaptation - Xuanting Lin; Wanjun Zhu; Longshen Liu; Zhenlei Zhou (Poultry Science)
With the advancement of precision livestock farming (PLF), acoustic technology has emerged as a key tool for tracking the health and well-being of laying hens, owing to its non-invasive, real-time and cost-effective nature. In this study, continuous audio data were collected from commercial chicken houses over a period of 15 days, in addition to temperature and humidity index (THI) analysis, to develop a convolutional neural network (CNN)-based model for classifying chicken squawks. This approach enabled the investigation of the relationship between environmental adaptability and acoustic traits in a mixed-sex rearing system. Significant daily variations were observed in the acoustic environment of the chicken house, with rooster crowing behavior corresponding to the highest noise levels (45–50 dB) recorded in the early morning hours. The CNN model achieved 98 % accuracy, along with both macro-average and micro-average scores of 98 %, in classifying roosters, hens, and other sounds, effectively addressing the issue of rooster crowing disturbances in mixed-rearing conditions. Additionally, the model revealed that fundamental frequency shift (F0 Shift) was positively correlated with normal egg production (r = 0.68, p = 0.025), while specific mel-frequency cepstral coefficients (MFCC_7, MFCC_10) associated with hen vocalization were significantly negatively correlated with THI ( r = -0.23, p < 0.05; r = -0.37, p < 0.001). These findings highlight the potential of acoustic monitoring as a novel dynamic method for evaluating environmental adaptability and health status in laying hens, reinforcing its utility in precision livestock farming under challenging rearing conditions.