Pestpix:
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Date
2025-11
Journal Title
Journal ISSN
Volume Title
Publisher
Don Mariano Marcos Memorial State University – Mid La Union Campus
Abstract
Agricultural 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.
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Keywords
Agriculture, Agriculture--Economic aspects, Agriculture and state, Farms, Agriculture--Research, Agricultural laborers, Farm management, Agricultural innovations, Agriculture--Technology transfer, Alternative agriculture, Agricultural industries--Technological innovations, Insect pests, Pests--Control, Insect pests--Control, Agricultural pests
Citation
Tangalin, 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 [Unpublished Undergraduate Thesis].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.