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All Journal JUSIFO : Jurnal Sistem Informasi
Widyatasya Agustika Nurtrisha
Department of Information System, Telkom University

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Artificial Intelligence for Precision Livestock Farming: A Systematic Review of Applications, Models, and Evaluation Metrics Widyatasya Agustika Nurtrisha; Luthfi Ramadani; Riska Yanu Fa’rifah; Faqih Hamami; Nur Ichsan Utama
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31179

Abstract

The increasing demand for animal-based food products has intensified the need for efficient, data-driven livestock management practices. Artificial Intelligence (AI) has emerged as a key enabling technology within Precision Livestock Farming (PLF), supporting automated monitoring, prediction, and decision-making processes. This study presents a Systematic Literature Review (SLR) of AI applications in livestock farming, focusing on application domains, AI models, and evaluation metrics. Following the PRISMA 2020 guidelines, relevant studies published between 2013 and 2024 were systematically identified, screened, and assessed across major scholarly databases, resulting in 20 eligible articles for qualitative synthesis. The findings indicate that AI is primarily applied to animal identification, body weight estimation, disease detection, behavior analysis, and feed management. Deep learning models, particularly Convolutional Neural Networks, dominate image-based tasks, while traditional machine learning approaches remain effective for structured sensor and tabular data. Common evaluation metrics include accuracy, precision, recall, R², and Mean Absolute Error. Despite promising results, the review reveals substantial heterogeneity in datasets, evaluation protocols, and livestock sector coverage, which limits cross-study comparability. This review highlights methodological trends, identifies key research gaps, and provides insights to guide future AI-driven PLF research and implementation.
Exploring the Affordances of AI-Enabled Livestock Monitoring Systems in Rural Agricultural Communities Luthfi Ramadani; Widyatasya Agustika Nurtrisha; Faqih Hamami; Nur Ichsan Utama; Riska Yanu Fa’rifah
JUSIFO : Jurnal Sistem Informasi Vol 11 No 2 (2025): JUSIFO (Jurnal Sistem Informasi) | December 2025
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v11i2.31181

Abstract

Productivity and sustainability remain persistent challenges in livestock farming across developing countries, particularly in rural contexts where digital transformation progresses unevenly. Advances in artificial intelligence (AI) offer opportunities to support livestock management; however, empirical understanding of how such technologies are perceived and utilized in rural settings remains limited. This study examines the perceived affordances of an AI-enabled livestock monitoring system in a rural community in Central Java, Indonesia. Guided by the Technology–Organization–Environment (TOE) framework, a qualitative case study approach was employed using semi-structured interviews with livestock farmers and local government officials. The findings indicate that the realization of AI-related affordances is shaped by technological conditions, including system capabilities, infrastructure limitations, and user readiness. Organizational factors—such as innovation awareness, government–community relationships, and the continuity of support programs—also influence affordance realization. Environmental conditions, particularly training adequacy, public trust, and rural geographic characteristics, further affect technology use. Overall, the study highlights that AI affordances in rural livestock systems are socio-technical and context-dependent, emphasizing the importance of context-sensitive design and implementation strategies to support sustainable livestock management.
Co-Authors Faqih Hamami Luthfi Ramadani Nur Ichsan Utama Riska Yanu Fa’rifah