VISIONARICE:

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creativework.keywordsleaf disease prediction, rice crop monitoring, smart farming technology, machine learning, image-based classification, web application system
dc.contributor.advisorPatacsil, Joseph A.
dc.contributor.authorLicudine, Aaron Jay C.
dc.contributor.authorDavid, Maria Luisa E.
dc.contributor.authorMadayag, Janvic B.
dc.contributor.authorSobredo, Irene P.
dc.contributor.chairNovencido, Denver A.
dc.contributor.committeememberBangug, Cristy M.
dc.contributor.committeememberColoma, Leah P.
dc.date.accessioned2026-04-22T08:05:56Z
dc.date.available2026-04-22T08:05:56Z
dc.date.issued2025-11
dc.descriptionFull text
dc.description.abstractThis study aimed to enhance rice crop health monitoring in La Union by developing a web-based system that applied machine learning for the early prediction of rice leaf diseases. It provided farmers and agriculture staff with timely and reliable insights to reduce yield losses and support sustainable crop management in the region. The researchers followed the CRISP-DM methodology, which included business understanding, data understanding, data preparation, modeling, evaluation, and deployment. System development was guided by the Extreme Programming (XP) model, enabling iterative prototyping with continuous user feedback. A convolutional neural network (CNN) using the ResNet architecture was trained on rice leaf images to classify common diseases such as leaf streak and blast, and it was integrated into a web-based application for real-time diagnosis and accessible information. Usability testing with 40 respondents, including farmers, agriculture staff, and IT experts, yielded high System Usability Scale (SUS) scores, indicating excellent usability, effectiveness, and strong user satisfaction.
dc.format.extentix, 87 p.: ill. (col.).
dc.identifier.citationLicudine, A. J. C., David, M. L. E., Madayag, J. B., & Sobredo, I. P. (2025). VISIONARICE: A machine learning approach for leaf disease prediction. [Unpublished Undergraduate Thesis].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/123456789/1366
dc.language.isoEnglish
dc.publisherDon Mariano Marcos Memorial State University - Mid La Union Campus
dc.rights.licenseCC BY 4.0
dc.sdgSDG 15
dc.subjectLeaves--Diseases and pests
dc.subjectResearch Network for Irrigation Management for Crop Diversification in Rice-Based Systems
dc.subjectDairy farming--Technological innovations
dc.subjectFarms--Technological innovations
dc.subjectDry farming--Technological innovations
dc.subjectFarm management--Technological innovations
dc.subjectFarm equipment--Technological innovations
dc.subjectFarms, Small--Technological innovations
dc.subjectOrganic farming--Technological innovations
dc.subjectMachine learning
dc.subjectMachine learning--Industrial applications
dc.subjectMachine learning--Technique
dc.subjectMachine learning--Graphic methods
dc.subjectMachine learning--Study and teaching
dc.subjectContent-based image retrieval
dc.subjectWeb applications
dc.titleVISIONARICE:
dc.title.alternativeA machine learning approach for leaf disease prediction
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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