AI-based early warning mechanism for poultry farms: Evaluating acoustic signal algorithms for bird health and sustainable production

Halleluyah Oluwatobi Aworinde ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 12, 108-117

Abstract: This study examines the rising consumption of animal products in Africa amid concerns about declining production. It suggests that food production needs to increase by 25% to meet the demands of a growing population. The experimental study involved 100 poultry birds divided into two groups: one inoculated with chronic respiratory disease (CRD), and one uninoculated. Over 65 days, audio signals were collected three times daily in a controlled environment with ethical approval. A nano 32 BLE sensor was used to collect a dataset of 346 audio signals from the farm. These signals were categorized as healthy, disease-related, or noisy. To identify the most effective model for early detection of poultry diseases, three algorithms utilizing audio signals were evaluated: MFCC, MFE, and Spectrogram. Results showed the Spectrogram algorithm outperformed others, with 99.2% accuracy, 99.3% F1-score, and 0.05 loss. The MFCC algorithm had 85.6% accuracy, 85% F1-score, and 0.38 loss, while the MFE algorithm achieved 97.4% accuracy, 97.3% F1-score, and 0.08 loss. Implementing it can support sustainable development goals 1 (No Poverty), 2 (Zero Hunger), 3 (Good Health and Well-being), and 12 (Responsible Consumption and Production) by improving poultry farming and reducing economic losses.

Keywords: Algorithms; Artificial intelligence; Audio signal processing; Deep learning techniques; Machine learning; Smart poultry. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:12:p:108-117:id:11019

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