Machine learning framework for poultry disease detection using vocal pattern analysis
Year: 2026
Type: article
Abstract: Current poultry health monitoring methods rely on visual inspection and manual assessment, which are laborious, unreliable, and frequently overlook early indicators of disease. Vocal Pattern Analysis is an innovative non-invasive method of automated health evaluation in poultry farming. This study analyzes vocal patterns to classify the health status of poultry, and builds and evaluates a machine learning system for a web-based real time classification system for this condition. A total of 346 audio samples were initially collected, comprising Healthy (n=139), Noise (n=86), and Unhealthy (n=121) recordings. After excluding 86 Noise samples as invalid data, 260 samples were used for binary classification (Healthy vs Unhealthy). Audio data extensive features extraction: 41 features composed from the Mel-frequency cepstral coefficients (MFCC), spectral features, zero crossing rate, chroma features, mel-spectrogram calculate statistics, RMS energy, and tempo. We compared two machine learning algorithms, Random Forest and Support Vector Machine (SVM). Both algorithms achieved 96.92% test accuracy, with macro-averaged metrics of 96.9% precision, 96.9% recall, and 96.9% F1-score. With regard to unhealthy detection, the Random Forest model achieved 96.67% sensitivity and SVM achieved 93.33% sensitivity, successfully differentiating healthy and unhealthy poultry vocalizations. We developed and deployed a web-based application that showcased the classification in real-time. It leads us to believe that ML based vocal pattern analysis can provide an effective, non-invasive method for poultry health monitoring. The system developed in this work could be applied for the automated health surveillance of commercial poultry.
Source: Smart Agricultural Technology
Author: Al Momen Pranta
Institution: Bangladesh Agricultural University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,770
OpenAlex ID: W7124979156