Year: 2025
Type: article
Abstract: This study investigates the acoustic characteristics of normal and abnormal calls in white-feathered broilers to propose a method for early detection of non-healthy conditions. Vocalizations were collected from 2-week-old broilers over a 21-day period and analyzed using time-domain and frequency-domain features, including maximum amplitude, effective amplitude, fundamental frequency, and pulse index. Significant differences were identified between normal calls and abnormal calls influenced by laryngeal mucus, with support vector machines and random forest classifiers achieving accuracies of 97.8% and 98.76%, respectively. Unlike previous empirical feature aggregation methods, this research employs statistically validated feature selection aligned with physiological mechanisms, enhancing interpretability and performance. The proposed framework offers a practical, automated solution for on-farm monitoring of broiler vocalizations, contributing to early detection of abnormal signs and improved management in precision poultry farming.
Authors: Bowen Sun, Xiangchao Kong, Xingkai Peng, Changxi Chen, Wanchao Zhang, Guangyu Zhao, Yongmin Guo, Kaisi Yang
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $323
OpenAlex ID: W4417450810