How AI Improves Sustainable Chicken Farming: A Literature Review of Welfare, Economic, and Environmental Dimensions

Zhenlong Wu, Sam Willems, Dong Liu and Tomas Norton ()
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Zhenlong Wu: M3 BIORES-Measure, Model and Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, Catholic University of Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
Sam Willems: M3 BIORES-Measure, Model and Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, Catholic University of Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
Dong Liu: M3 BIORES-Measure, Model and Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, Catholic University of Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
Tomas Norton: M3 BIORES-Measure, Model and Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, Catholic University of Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium

Agriculture, 2025, vol. 15, issue 19, 1-24

Abstract: Artificial Intelligence (AI) is widely recognized as a force that will fundamentally transform traditional chicken farming models. It can reduce labor costs while ensuring welfare and at the same time increase output and quality. However, the breadth of AI’s contribution to chicken farming has not been systematically quantified on a large scale; few people know how far current AI has actually progressed or how it will improve chicken farming to enhance the sector’s sustainability. Therefore, taking “AI + sustainable chicken farming” as the theme, this study retrieved 254 research papers for a comprehensive descriptive analysis from the Web of Science (May 2003 to March 2025) and analyzed AI’s contribution to the sustainable in recent years. Results show that: In the welfare dimension, AI primarily targets disease surveillance, behavior monitoring, stress detection, and health scoring, enabling earlier, less-invasive interventions and more stable, longer productive lifespans. In economic dimension, tools such as automated counting, vision-based weighing, and precision feeding improve labor productivity and feed use while enhancing product quality. In the environmental dimension, AI supports odor prediction, ventilation monitoring, and control strategies that lower emissions and energy use, reducing farms’ environmental footprint. However, large-scale adoption remains constrained by the lack of open and interoperable model and data standards, the compute and reliability burden of continuous multi-sensor monitoring, the gap between AI-based detection and fully automated control, and economic hurdles such as high upfront costs, unclear long-term returns, and limited farmer acceptance, particularly in resource-constrained settings. Environmental applications are also underrepresented because research has been overly vision-centric while audio and IoT sensing receive less attention. Looking ahead, AI development should prioritize solutions that are low cost, robust, animal friendly, and transparent in their benefits so that return on investment is visible in practice, supported by open benchmarks and standards, edge-first deployment, and staged cost–benefit pilots. Technically, integrating video, audio, and environmental sensors into a perception–cognition–action loop and updating policies through online learning can enable full-process adaptive management that improves welfare, enhances resource efficiency, reduces emissions, and increases adoption across diverse production contexts.

Keywords: chicken welfare; production economics; environmental impact; sustainable chicken farming; precision livestock farming; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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