Evidence map›Paper›PMID 42121705›Full record

ReviewAnimals : an open access journal from MDPI2026

A Narrative Review on Internet of Things and Artificial Intelligence for Poultry Production.

Anjan Dhungana, Bidur Paneru, Samin Dahal, Lilong Chai

Abstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anjan DhunganaDepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0002-8959-8293
Bidur PaneruDepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.
Samin DahalDepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.
Lilong ChaiDepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0002-5378-6727

Funding

United States Department of Agriculture 2023-68008-39853
6 · The paper itself

Abstract

Recently, poultry production has increased worldwide to address the increasing demand of affordable animal-sourced protein. To meet this requirement, poultry production operations have become more concentrated, introducing management challenges related to disease control, productivity, and animal welfare. However, manual flock monitoring and management have become impractical in such cases, creating a need for automatic data-driven management approaches. In this context, the Internet of Things (IoT) has emerged as a potential technological solution for continuous flock monitoring, data sharing, and decision-making. Despite this, its adoption in poultry production is limited compared with its widespread use in crop production, transportation, and manufacturing industrial sectors. Furthermore, advanced analytical techniques such as artificial intelligence (AI), applied to data gathered by IoT-enabled devices, have shown promising results by generating actionable information. Existing literature suggests that the integration of IoT and AI can address the major challenges associated with modern large-scale poultry production systems. While most applications remain at the research scale, such technologies have the potential for improving flock monitoring, enhancing productivity, and ensuring proper animal welfare. This narrative review examines the current state of IoT and AI based technologies, together or in part identifies the limitations, research gaps, and opportunities for future development.

Indexed as

cyber-physical systemsdeep learningpoultry productionprecision farming

Identifiers

PMID42121705
PMCPMC13162782

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.