An Acoustic Feature-Based Ensemble Learning Approach for Chicken Health Detection
Received: 24 December 2025 | Revised: 1 February 2026 | Accepted: 11 February 2026 | Online: 28 February 2026
Corresponding author: Tri Kuntoro Priyambodo
Abstract
Early disease detection in commercial poultry farms is critical for preventing outbreaks and minimizing economic losses. Conventional inspection is labor-intensive and frequently results in delayed diagnosis. This paper proposes an ensemble machine learning system for automated binary broiler health classification and evaluates the feasibility of non-invasive vocalization-based monitoring using acoustic analysis. Audio recordings were collected from 17–30-day-old broiler chickens in two closed-house commercial facilities: an academic research farm at the Faculty of Animal Science, Universitas Gadjah Mada (Yogyakarta), and a commercial farm in Blitar, Indonesia. Individual birds were recorded in isolated pens to eliminate background noise and ensure signal quality. A total of 22 acoustic features were extracted, comprising Mel-Frequency Cepstral Coefficients (13 features), time-domain features (5 features), and frequency-domain features (4 features). Three machine learning algorithms (SVM, Random Forest, and Logistic Regression) were evaluated across seven feature combinations using 5-fold cross-validation. Random Forest with MFCC features achieved the best individual performance (96.49% F1-score). An ensemble classifier with weighted soft voting was developed, integrating SVM (Time+MFCC), Logistic Regression (Time+Frequency+MFCC), and Random Forest (MFCC), with optimal weights determined through grid search, achieving 98.29% F1-score and 98.25% accuracy, outperforming the individual models. The high classification F1-score and accuracy demonstrate the feasibility of acoustic-based health monitoring for broiler chickens under controlled recording conditions to support early disease detection.
Keywords:
acoustic monitoring, ensemble learning, poultry health, disease detectionDownloads
References
A. Dhakal, S. Devkota, S. B. Jethara, R. K. Yadav, and P. Phuyal, "Assessment of Biosecurity in Poultry Farms in Chitwan, Nepal," Veterinary Medicine and Science, vol. 11, no. 2, Mar. 2025, Art. no. e70232.
A. Banakar, M. Sadeghi, and A. Shushtari, "An intelligent device for diagnosing avian diseases: Newcastle, infectious bronchitis, avian influenza," Computers and Electronics in Agriculture, vol. 127, pp. 744–753, Sept. 2016.
L. Carpentier, E. Vranken, D. Berckmans, J. Paeshuyse, and T. Norton, "Development of sound-based poultry health monitoring tool for automated sneeze detection," Computers and Electronics in Agriculture, vol. 162, pp. 573–581, July 2019.
K. Cuan, T. Zhang, J. Huang, C. Fang, and Y. Guan, "Detection of avian influenza-infected chickens based on a chicken sound convolutional neural network," Computers and Electronics in Agriculture, vol. 178, Nov. 2020, Art. no. 105688.
R. O. Ojo, A. O. Ajayi, H. A. Owolabi, L. O. Oyedele, and L. A. Akanbi, "Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review," Computers and Electronics in Agriculture, vol. 200, Sept. 2022, Art. no. 107266.
J. Huang, T. Zhang, K. Cuan, and C. Fang, "An intelligent method for detecting poultry eating behaviour based on vocalization signals," Computers and Electronics in Agriculture, vol. 180, Jan. 2021, Art. no. 105884.
A. Mahdavian, S. Minaei, P. M. Marchetto, F. Almasganj, S. Rahimi, and C. Yang, "Acoustic features of vocalization signal in poultry health monitoring," Applied Acoustics, vol. 175, Apr. 2021, Art. no. 107756.
A. Mao et al., "Automated identification of chicken distress vocalizations using deep learning models," Journal of The Royal Society Interface, vol. 19, no. 191, June 2022, Art. no. 20210921.
Z. Sun, M. Zhang, J. Liu, J. Wang, Q. Wu, and G. Wang, "Research on white feather broiler health monitoring method based on sound detection and transfer learning," Computers and Electronics in Agriculture, vol. 214, Nov. 2023, Art. no. 108319.
J. Huang, W. Wang, and T. Zhang, "Method for detecting avian influenza disease of chickens based on sound analysis," Biosystems Engineering, vol. 180, pp. 16–24, Apr. 2019.
Z. Sun, W. Tao, M. Gao, M. Zhang, S. Song, and G. Wang, "Broiler health monitoring technology based on sound features and random forest," Engineering Applications of Artificial Intelligence, vol. 135, Sept. 2024, Art. no. 108849.
K. Cuan, T. Zhang, Z. Li, J. Huang, Y. Ding, and C. Fang, "Automatic Newcastle disease detection using sound technology and deep learning method," Computers and Electronics in Agriculture, vol. 194, Mar. 2022, Art. no. 106740.
T. G. Dietterich, "Ensemble Methods in Machine Learning," in Multiple Classifier Systems, 2000, pp. 1–15.
A. Husin, "Designing Multiple Classifier Combinations: A Survey," Journal of Theoretical and Applied Information Technology, vol. 97, no. 20, pp. 2386–2405, Oct. 2019.
A. Stefanowska and S. K. Zielinski, "Speech Emotion Recognition Using a Multi-Time-Scale Approach to Feature Aggregation and an Ensemble of SVM Classifiers," Archives of Acoustics, pp. 153–168, May 2024.
H. Nawang, C. A. C. Yahaya, R. M. Sumeri, and K. A. Abdullah, "Weighted majority voting ensemble method for student performance classification," International Journal of Advanced Technology and Engineering Exploration, vol. 12, no. 129, Aug. 2025.
E. M. Hill, G. Koay, R. S. Heffner, and H. E. Heffner, "Audiogram of the chicken (Gallus gallus domesticus) from 2 Hz to 9 kHz," Journal of Comparative Physiology A, vol. 200, no. 10, pp. 863–870, Oct. 2014.
B. Krumm, G. M. Klump, C. Köppl, R. Beutelmann, and U. Langemann, "Chickens have excellent sound localization ability," Journal of Experimental Biology, vol. 225, no. 5, Mar. 2022, Art. no. jeb243601.
V. L. Hardjanto and Wahyono, "Audio Enhancement for Gamelan Instrument Recognition using Spectral Subtraction," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 22042–22048, Apr. 2025.
W. Tao, G. Wang, Z. Sun, S. Xiao, Q. Wu, and M. Zhang, "Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model," Sensors, vol. 22, no. 20, Oct. 2022, Art. no. 7935.
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sept. 1995.
C. Y. J. Peng, K. L. Lee, and G. M. Ingersoll, "An Introduction to Logistic Regression Analysis and Reporting," The Journal of Educational Research, vol. 96, no. 1, pp. 3–14, Sept. 2002.
Downloads
How to Cite
License
Copyright (c) 2026 Novita Rosyida, Tri Kuntoro Priyambodo, Afiahayati, Zuprizal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
