UNAScan:

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creativework.keywordsbasi production, deep learning, image classification, MobileNetV3, sugarcane leaf disease
dc.contributor.advisorEstira, Marydel C.
dc.contributor.authorBernal, Yhoebe Rae C.
dc.contributor.authorAcocos, Kyla Q.
dc.contributor.authorGarcia, Jasmin M.
dc.contributor.authorKimayong, Princess Joy D.
dc.contributor.chairMique Jr., Eusebio L.
dc.contributor.committeememberMalicdem, Alvin R.
dc.contributor.committeememberPulmano, Dominador G.
dc.date.accessioned2026-04-22T01:20:03Z
dc.date.available2026-04-22T01:20:03Z
dc.date.issued2025-11
dc.descriptionFull text
dc.description.abstractThis study was conducted to support sugarcane farmers in detecting and managing leaf diseases that affect crop quality and yield, especially for Basi production. The capstone project aimed to build a dataset of sugarcane leaf images and train an optimized deep learning model using the MobileNetV3 architecture. The modeling and development process followed the CRISP-DM framework, ensuring structured stages from data understanding to model evaluation. The trained model was then integrated into both web and mobile platforms through the Agile Methodology. A descriptive and developmental research design was applied with twenty (20) respondents: five (5) agricultural staff, five (5) Basi producers, and ten (10) sugarcane farmersfrom Naguilian, La Union. The system achieved a 98% accuracy rate and a System Usability Scale (SUS)score of 85.6, indicating excellent usability. Respondents described the system as efficient, user-friendly, and highly valuable for early detection and effective management of common sugarcane leaf diseases.
dc.format.extentxii, 82 p.: ill. (col.).
dc.identifier.citationBernal, Y. R. C., Acocos, K. Q., Garcia, J. M., & Kimayong, P. J. D. (2025). UNAScan: Applying deep learning for early detection of sugarcane leaves diseases for basi production in Naguilian, La Union.[Unpublished Undergraduate Thesis]. Don Mariano Marcos Memorial State University - Mid La Union Campus, City of San Fernando, La Union. Lakasa ti Sirib, DMMMSU Institutional Repository.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1362
dc.language.isoEnglish
dc.publisherDon Mariano Marcos Memorial State University - Mid La Union Campus
dc.rights.licenseCC BY 4.0
dc.sdgSDG 9
dc.sdgSDG 12
dc.subjectSugarcane products
dc.subjectSugarcane products--Analysis
dc.subjectDeep learning (Machine learning)
dc.subjectSugarcane leaf scald
dc.titleUNAScan:
dc.title.alternativeApplying deep learning for early detection of sugarcane leaves diseases for basi production in Naguilian, La Union
dc.typeThesis
dcterms.accessRightsOpen access
thesis.degree.disciplineCollege of Information Technology
thesis.degree.levelUndergraduate
thesis.degree.nameBachelor of Science in Information Technology
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