LAKASABrowsing by Author "David, Maria Luisa E."

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(Don Mariano Marcos Memorial State University - Mid La Union Campus, 2025-11) Licudine, Aaron Jay C.; David, Maria Luisa E.; Madayag, Janvic B.; Sobredo, Irene P.; Patacsil, Joseph A.; Novencido, Denver A.; Bangug, Cristy M.; Coloma, Leah P.
This 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.