A systematic review of artificial intelligence in small ruminant production systems: applications, performance outcomes, and reported implementation challenges
Abstract
Background: Sheep and goats are essential components of global livestock systems, supporting smallholder livelihoods and contributing substantially to meat, milk, and fiber production. However, many small ruminant production systems remain extensive and constrained by limited infrastructure, labor availability, and challenges in continuous animal monitoring. Artificial Intelligence (AI) offers new opportunities for precision livestock management through automated data analysis, early detection of production- and health-related changes, and improved decision support. This systematic review aimed to characterize current AI applications in sheep and goat production, summarize reported performance outcomes across all major application domains, and identify barriers affecting practical implementation.
Results: A systematic search of Web of Science, Scopus, and PubMed identified peer-reviewed studies published between January 2020 and December 2025. From 11,035 records screened, 92 studies met the inclusion criteria and were synthesized narratively; meta-analysis was not conducted due to substantial methodological heterogeneity across studies. AI applications spanned six domains: behavior and activity recognition (26.1%, n = 24; mean accuracy 92.4%, range 66.7-100%), individual animal identification (19.6%, n = 18; mean accuracy 97.3%, range 93.3-99.9%), health, welfare, and disease detection (19.6%, n = 18; mean accuracy 89.7%, range 62.0-99.0%), growth and body measurement…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in animal, not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 24 participants or records.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.