PS11-5. Artificial Intelligence-Powered Poultry Disease Prediction Model.

Clicks: 9
ID: 330362
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #837 of 847 articles by views in italian journal of animal science

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 847 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract One of the most devastating incidents in Bangladesh is the outbreak of Avian Influenza. Due to this epidemic, a large number of poultry birds die every year. As a result, this country loses a valuable source of affordable poultry meat, which contributes significantly to meeting the growing demand for protein. Currently, most commercial poultry farms rely on monitoring through CCTV Cameras. While this allows observation from remote locations, it does not support effective analysis of poultry health data, nor does it ensure proper feed and water management, air quality improvement, and temperature regulation. To address these challenges, this research proposes the introduction of AI-powered sensors and real-time statistical data analysis. The system I envision will continuously monitor a multi-parameter environmental profile—temperature, humidity, atmospheric pressure, light intensity, and gas concentration, including ammonia (NH3) levels which correlate strongly with flock health deterioration—and cross-reference these streams against curated baseline health datasets. A predictive machine learning layer would identify statistical deviations and biological anomalies, generating a probabilistic outbreak forecast which would dispatch alerts to poultry researchers to enable immediate quarantine of the infected birds to ensure biosecurity. I aim to build a collaborative national poultry health intelligence network in Bangladesh, where collected data across thousands of farms continuously improves the underlying predictive model, making the system more accurate and adaptive with every deployment cycle. Therefore, it is essential to overcome key challenges in poultry farming, such as preventing avian influenza and implementing environmentally sustainable management systems. In conclusion, the application of artificial intelligence in poultry farming can reduce production costs and significantly improve profit margins.
Reference Key
openalex_W7214741083 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag272.340
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.