AI and Data Analytics in the Dairy Farms: A Scoping Review

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dc.article.number1291
dc.catalogadorpva
dc.contributor.authorPalma, Osvaldo
dc.contributor.authorPlà-Aragonés, Lluis M.
dc.contributor.authorMac Cawley Vergara, Alejandro Francisco
dc.contributor.authorAlbornoz, Víctor M.
dc.date.accessioned2025-05-16T19:52:39Z
dc.date.available2025-05-16T19:52:39Z
dc.date.issued2025
dc.description.abstractThe strong growth of the world population will cause a major increase in demand for bovine milk, making it necessary to use various technologies to increase milk production efficiently. Some technologies that can contribute to solving part of this problem are those related to data analytics tools, big data, and sensor development. It is timely to review modern technologies and data analytics methods for milk predictions in view of supporting decision-making in dairy farms. To this end, a scoping review was carried out, which resulted in 151 articles of interest. Among the most important results, we found that (i) the identified studies are relatively recent with an average publication time of 5.95 years; (ii) the scope of the selected studies is mostly concentrated on milk and prediction (29%), early detection of lameness (26%), and timely detection of mastitis (13%); (iii) the type of analysis is mostly predictive (87%), and prescriptive is barely present (3%); (iv) the types of input data used in the studies are preferably historical (70%), and real-time data (25%) are used less frequently; (v) we found that the method of artificial neural networks (47%) and the convolutional neural networks (24%) are the most used for the studies regarding bovine milk output predictions. In the selected studies, the artificial neural network methods have considerable accuracy, recall, precision, and F1 Scores on average but with high ranges and standard deviations. (vi) Simulation tools are scarcely used, being present in 4% of cases. In the treatment of variability, the models reviewed are mostly deterministic (77%), and the stochastic models (5%) are considered in a small number of cases. Based on our analysis, we conclude that future research on decision-making tools will benefit from the advantages of artificial neural networks in data analytics combined with optimization–simulation methods.
dc.fechaingreso.objetodigital2025-05-16
dc.format.extent34 páginas
dc.fuente.origenORCID
dc.identifier.doi10.3390/ani15091291
dc.identifier.urihttps://doi.org/10.3390/ani15091291
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104355
dc.information.autorucFacultad de Agronomía e Ingenieria Forestal; Mac Cawley Vergara, Alejandro Francisco; 0000-0002-4848-4732; 81775
dc.issue.numero9
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherMDPI
dc.revistaAnimals
dc.rightsacceso abierto
dc.rights.licenseAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectMilk production
dc.subjectMachine learning
dc.subjectData analytics
dc.subjectNeural networks
dc.subjectSimulation
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleAI and Data Analytics in the Dairy Farms: A Scoping Review
dc.typeartículo de revisión
dc.volumen15
sipa.codpersvinculados81775
sipa.trazabilidadORCID;2025-05-07
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