Adoption of Artificial intelligence (AI) in animal husbandry: A systematic literature review
Muhammad Helmi (a), Nanang Febrianto (a), Priyo Sugeng Winarto (a), Marji (b), Budi Hartono (a*)

a) Department of Livestock Socio-Economics, Faculty of Animal Science, Universitas Brawijaya, Malang 65145, Indonesia
b) Faculty of Computer Science, Universitas Brawijaya, Malang 65145, Indonesia

Abstract

Rapid growth in global demand for animal products, climate change pressures, and labor constraints have increased the urgency for innovation in livestock management. Artificial intelligence (AI) offers potential to transform animal husbandry through enhanced precision, efficiency, and animal welfare. However, adoption across livestock systems remains uneven and poorly understood. This systematic review aims to identify AI technologies applied in animal farming, assess factors influencing their adoption, and evaluate integration challenges and strategic responses. Using PRISMA guidelines and Tranfield^s framework, a structured literature search was conducted in Web of Science, Scopus, and ScienceDirect, focusing on English-language peer-reviewed articles from 2015 to 2025. The PICO framework guided article selection, resulting in 29 empirical studies assessed using the Mixed Methods Appraisal Tool (MMAT). The review finds that machine learning, computer vision, and IoT-based systems are most widely implemented, especially in dairy and swine farming. Key adoption factors include technical compatibility, economic feasibility, social readiness, and infrastructure availability. Economic barriers such as high upfront costs and uncertain returns were most frequently reported. Implementation challenges include data quality, system integration, limited digital literacy, and rural connectivity issues. Strategic responses identified include phased adoption, edge computing, training programs, and policy support. The study highlights research gaps in long-term performance evaluation, multi-technology integration, and social-behavioral aspects of adoption. Future research should prioritize cost-effective, scalable AI models tailored to small and medium farms and develop standardized metrics for technology assessment. These findings offer practical guidance for stakeholders aiming to enable responsible and inclusive AI adoption in livestock production systems.

Keywords: Adoption- Animal Husbandry- Artificial Intelligence (AI)- Smart Farming- review

Topic: Animal agribusiness and related subject