Publication:
Grapex:

Simple item page
dc.contributor.advisorEstira, Marydel
dc.contributor.authorBacani, Janverly Mhaye O.
dc.contributor.authorAmparo, Arianne Therese S.
dc.contributor.authorAberin, Lexter C.
dc.contributor.authorLaranang, Hardly Rianne N.
dc.contributor.chairMique, Eusebio L.
dc.contributor.committeememberPatacsil, Joseph A.
dc.date.accessioned2026-03-13T06:54:02Z
dc.date.available2026-03-13T06:54:02Z
dc.date.issued2025-11
dc.description.abstractThis study developed GRAPEX, a digital tool for the efficient, early detection of critical fungal diseases like Black Rot and Black Measles (Esca) in grape leaves, primarily for farmers in Bauang, La Union. The methodology utilized descriptive and applied research, adhering to the CRISP-DM framework for model training and Extreme Programming (XP) for application development. Key goals included creating a validated grape leaf disease dataset, training a Convolutional Neural Network (CNN), developing a multi-platform application, and assessing system usability. The CNN model achieved a high classification accuracy of 90.3%. Furthermore, the system attained an Excellent Usability rating across all user groups (20 respondents): five (5) agricultural staff, five (5) IT Experts, and ten (10) grape farmers from Bauang, La Union, yielding an average System Usability Scale (SUS) score of 81. The GRAPEX system successfully detected Black Measles, Black Rot, and healthy conditions, proving to be a reliable, efficient, and user- friendly tool with significant potential for vineyard management.
dc.identifier.citationBacani, J. M. O., Amparo, A. T. S., Aberin, L. C., & Laranang, H. R. N. (2024) [Unpublished Undergraduate Feasibility Study]. Don Mariano Marcos Memorial State University – Mid La Union Campus. Lakasa ti Sirib, DMMMMSU Institutional Repository.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1194
dc.language.isoen
dc.publisherDon Mariano Marcos Memorial State University – Mid La Union Campus
dc.sdgSDG 9
dc.titleGrapex:
dc.title.alternativeDeep learning-based image analysis for black measles and black rot detection in grape leaves
dc.typeThesis
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
UT-MLUC-2025-CIT-BSIT-BacaniJM-FT.pdf
Size:
44.62 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Undergraduate Theses