The Impact of Artificial Intelligence Techniques on The Growth and Carcass Performance of Broiler Chickens, A Review

Rebh, Ahmed Yasir; Al-Hmedawy, Noora Karim; Alasadi, Majid Hassan.

doi:10.36582/ksj.2026.170130.1046

	The Impact of Artificial Intelligence Techniques on The Growth and Carcass Performance of Broiler Chickens, A Review
Al-Kunooze Scientific Journal
Volume 12, Issue 3, July 2026, Pages 266-277 PDF (711.01 K)
Document Type: Review
DOI: 10.36582/ksj.2026.170130.1046
Authors
ahmed yasir rebh^* ¹; Noora karim Al-Hmedawy²; Majid Hassan. Alasadi³
¹Dept of Animal Production- Shatra Technical institute ,Southern Technical University .Iraq
²Department of Animal Production, College of Agriculture, University of Dhi Qar.Iraq
³Dept. of Animal Production College of Agriculture, University of Basrah, Iraq
Abstract
The efficiency and productivity in many areas such as animal science could increasingly be improved by artificial intelligence (AI). Because generating and analyzing large amounts of data in real time is now feasible, the use of smart technologies in modern farming systems is gaining ground, which further strengthens AI’s role in livestock production. Machine learning (ML) is one of the main types of AI that enables a computer to learn through a dataset and predict a test outcome without being explicitly programmed to do so. Predicting outcomes based on an input data is statistically, the outcome of a machine learning algorithm. Machine learning methods show better prediction performance in broiler production. Studies have shown that broiler growth and body weight can be predicted with 98% to 99%. In addition, neural network models detected the presence or absence of ascites in broiler chickens with 100% effectiveness. When machine-vision techniques were incorporated into SVM models, accuracy reached 99.5% for identifying healthy and avian influenza infected chickens in the SVM literature (Arowolo, 2017). It may be concluded that ML is likely to play an essential role in broiler growth prediction and health disorders detection. As a result, the study seeks to examine broiler growth and health predictions through machinelearning techniques. Due to its ability to deal effectively with large datasets and model nonlinear relationships appropriately, ML has great potential to complement or replace conventional approaches in poultry-production systems of the future.
Keywords
Artificial intelligence; production efficiency; machine learning; broiler chicken; body weight

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