Agripedia: a deep learning approach for crop health monitoring using yolov5 and vgg-16-based leaf disease detection/ Jhan Kyle V. Agullo, Deniece Winslhet A. Gabaon, John Patrick I. Marasigan, and Matthew A. Navale.--

Bachelor's thesis

College of Science.--
Bachelor of science in computer science: Technological University of the Philippines,
2025.

Includes bibliographic references and index.

Agriculture is a vital sector in the Philippines, employing 25% of the workforce and
contributing 8.9% to the GDP. However, challenges such as climate change, pests, and limited
access to expert knowledge hinder productivity, especially among small-scale farmers. To address
this, Agripedia–an IoT-enabled crop monitoring system–was developed to promote backyard
farming by providing real-time environmental tracking and AI-driven disease detection. The
system integrates Arduino-based sensors (temperature, humidity, soil moisture and light intensity)
with the help of a camera to assess crop health, an object detection model: YOLOv5 which
detects leaves from images captured on the camera. while a convolutional neural network (CNN)
analyzes leaf images to identify diseases. The mobile app processes this data, offering actionable
recommendations to optimize yield and prevent crop loss. Agripedia also features a crop database
with planting guides, tailored for Filipino users to enhance food security during crises like
pandemics and typhoons. The system was evaluated through demonstrations and surveys
distributed to 30 respondents (backyard farmers, agricultural workers, and IT professionals) based
on ISO 25010 standards. Results showed strong agreement in functionality, usability, reliability,
and maintainability. By democratizing access to precision farming tools Agripedia aims to
empower communities, improve crop resilience, and contribute to sustainable agriculture in the
Philippines.