Application of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samples

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dc.contributor.authorQuispe Bonilla, Max David
dc.contributor.authorSerrano Arriezu, Luis Javier
dc.contributor.authorTrigo Vilaseca, Jesús Daniel
dc.contributor.authorQuispe Bonilla, Christian
dc.contributor.authorPoma Gutiérrez, Adolfo
dc.contributor.authorQuispe Peña, Edgar
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzareneu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes
dc.date.accessioned2023-01-10T09:36:54Z
dc.date.available2023-01-10T09:36:54Z
dc.date.issued2022
dc.date.updated2023-01-10T09:22:17Z
dc.description.abstractThe aim of this research is to develop and validate two computer programs based on artificial intelligence (AI) and digital image analysis (DIA) in order to determine the incidence of medullation in white alpaca fibers. Two data sets were analyzed: 76 samples of Huacaya alpaca fibers obtained from Huancavelica, Peru, and 200 samples of white alpacas of two genotypes (Huacaya, n =100; Suri, n = 100), obtained from Arequipa, Peru. The preparation of each sample followed the procedure described in IWTO-8-2011. The Pytorch framework was used to generate several training models based on the You Only Look at Once (YOLO) architecture. Circa 4000 pictures of fibers were taken and 661 of them were selected as representative. Using the LabelImg software, the fibers present in each representative picture (approximately 10 fibers/picture) were labeled as one of these two classes: either medullated or non-medullated. Subsequently, the data augmentation technique was applied to expand the data set to 3966 photographs. Thus, 90 of them were used as initial validation data, while the reaming 3876 pictures (containing a total of 23,964 labeled fibers) were used as training data. Matlab was used to develop the DIA-based software. More specifically, algorithms of pre-processing, segmentation, smoothing, skeletonization and Hough transform were implemented to detect medullated and non-medullated fibers. Correlation and linear regression analyses were used to evaluate the models. The medullation percentage results show that there is no statistically significant difference between the AI-based method and the projection microscope method (p-value = 0.668 and 0.672 for the t-student and Wilcoxon tests, respectively). Moreover, the correlation of each of the developed computer methods with the projection microscope method is very strong (r = 0.99 and 0.97). This confirms the software ability to perform the recognition of fibers with and without medullation. Similar results (p-value = 0.357) were obtained when comparing the projection microscope method and DIA-based software method. Finally, using the proposed framework, the average time required to analyze a sample was 19.44 s. As a result, this software allows the implementation of practical, precise, and efficient methodologies to determine the incidence of medullation of alpaca fibers.eu
dc.description.sponsorshipOpen Access funding provided by Public University of Navarre, Spain.en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBonilla, M. Q., Serrano-Arriezu, L., Trigo, J. D., Bonilla, C. Q., Gutiérrez, A. P., & Peña, E. Q. (2022). Application of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samples. Small Ruminant Research, 213, 106724. https://doi.org/10.1016/j.smallrumres.2022.106724en
dc.identifier.doi10.1016/j.smallrumres.2022.106724
dc.identifier.issn0921-4488
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/44549
dc.language.isoengen
dc.publisherElsevier Scienceen
dc.relation.ispartofSmall Ruminant Research 213 (2022) 106724en
dc.relation.publisherversionhttps://doi.org/10.1016/j.smallrumres.2022.106724
dc.rights© 2022 The Author(s). This is an open access article under the CC BY-NC-ND licenseen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDeep learningen
dc.subjectMedullaen
dc.subjectObjectable fiberen
dc.subjectSouth American camelidsen
dc.titleApplication of artificial intelligence and digital images analysis to automatically determine the percentage of fiber medullation in alpaca fleece samplesen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationc69a5f75-e455-4367-b641-e335451fac03
relation.isAuthorOfPublication21ab7bc6-f6ae-427d-938f-24452b396567
relation.isAuthorOfPublication92c20ba0-e062-4195-9962-5a0111a7e879
relation.isAuthorOfPublication.latestForDiscovery21ab7bc6-f6ae-427d-938f-24452b396567

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