Digital Transformation of Poultry Farming Through Artificial Intelligence - Laeli Fitrah; Hari Setiabudi Husni (Asosiasi Dosen Muda Indonesia, 2025)
The global poultry sector is under pressure to increase efficiency, sustainability, and animal welfare amid growing demand and resource constraints. Artificial Intelligence (AI) has emerged as a key enabler of digital transformation in poultry farming, yet evidence on practical adoption remains fragmented, especially for smallholder and UMKM contexts. Objective: This study systematically maps AI applications in poultry farming, classifies their functional domains and technological approaches, evaluates reported benefits and limitations, and identifies research gaps related to real-world implementation. Method: A PRISMA 2020-guided Systematic Literature Review (SLR) was conducted on 28 peer-reviewed, open-access, Scopus-indexed journal articles published between 2020 and 2025. Data were extracted on AI techniques, data modalities, application domains, implementation settings, and reported outcomes, then synthesized using thematic analysis. Findings: AI applications concentrate on disease detection and health monitoring, environmental control, behavior and welfare analysis, feed optimization, and productivity forecasting. Deep learning and computer vision dominate image/video-based tasks, while conventional machine learning supports multivariate prediction. Most studies report laboratory or pilot validation rather than full field deployment. Common barriers include high initial costs, limited digital literacy, infrastructure constraints (e.g., connectivity), and scarce localized datasets challenges that are particularly salient in developing-country settings. Implications: Adoption is most feasible through affordable, modular monitoring and decision-support solutions, supported by local dataset development, capacity building, and multi-stakeholder partnerships to translate pilots into sustained deployments. Originality/Value: This review integrates functional classification, technological mapping, and implementation maturity into a unified framework, offering an operational perspective on how AI can be scaled inclusively in poultry farming.