Pestpix:

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creativework.keywordsagriculture, agricultural technology, pest detection
dc.contributor.advisorSardeng, Shekainah Kim A.
dc.contributor.authorTangalin, Mezrhime Kyle B.
dc.contributor.authorFortes, John Reymond N. .
dc.contributor.authorLumaad, Zyla Rea D.
dc.contributor.authorVallo, Maria Juliana N
dc.contributor.chairHortizuela, Manny R.
dc.contributor.committeememberRodriguez, Marylen D.
dc.contributor.committeememberBajit, Danilo T.
dc.date.accessioned2026-03-13T02:18:08Z
dc.date.available2026-03-13T02:18:08Z
dc.date.issued2025-11
dc.descriptionFull text
dc.description.abstractAgricultural productivity is continuously threatened by pests and diseases, demanding rapid and accurate identification to safeguard global food security. Traditional expert-dependent methods are often slow, hindering timely intervention. This study addressed this challenge by developing and implementing the PESTPIX mobile application for vegetable pest identification using machine learning. The research employed descriptive and applied research designs, applying the CRISP-DM framework for the machine learning model development and the Agile methodology for application development, ensuring a user-centric and iterative process. The level of usability was assessed through the PSSUQ, resulting to high usability which confirmed the application's ease of use, functional completeness, and clear content, and validated its practical value and high potential for successful adoption by Filipino vegetable farmers. The successful integration of a machine learning solution into a mobile platform offered an accessible solution to strengthen sustainable agricultural practices and directly contribute to increased small-scale farmer productivity.
dc.format.extentxi, 78 p.: ill. (col.).
dc.identifier.citationTangalin, M. K. B., Fortes, J. R. N., Lumaad, Z. R. D., & Vallo, M. J. N. (2025). Pestpix: A mobile application for quick crop pest identification and management using convolutional neural networks. [Unpublished Undergraduate Thesis].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/123456789/1186
dc.language.isoEnglish
dc.publisherDon Mariano Marcos Memorial State University – Mid La Union Campus
dc.rights.licenseCC BY 4.0
dc.sdgSDG 2
dc.subjectAgriculture
dc.subjectAgriculture--Economic aspects
dc.subjectAgriculture and state
dc.subjectFarms
dc.subjectAgriculture--Research
dc.subjectAgricultural laborers
dc.subjectFarm management
dc.subjectAgricultural innovations
dc.subjectAgriculture--Technology transfer
dc.subjectAlternative agriculture
dc.subjectAgricultural industries--Technological innovations
dc.subjectInsect pests
dc.subjectPests--Control
dc.subjectInsect pests--Control
dc.subjectAgricultural pests
dc.titlePestpix:
dc.title.alternativeA mobile application for quick crop pest identification and management using convolutional neural networks
dc.typeThesis
dcterms.accessRightsOpen access
thesis.degree.disciplineCollege of Infomation Technology
thesis.degree.levelUndergraduate
thesis.degree.nameBachelor of Science in Information Technology
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