Publication: Grapex:
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Date
2025-11
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Don Mariano Marcos Memorial State University – Mid La Union Campus
Abstract
This 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.
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Citation
Bacani, J. M. O., Amparo, A. T. S., Aberin, L. C., & Laranang, H. R. N. (2024) [Unpublished Undergraduate Feasibility Study].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.