Agroai: Leaf-Based Crop Disease Detection for Brgy. Lidong Farmers Using Convolutional Neural Networks, Computer Vision, and Data Analytics

Abstract

The output of the study is an application called AgroAI: Leaf-Based Crop Disease Detection System. This web-based and mobile-enabled system is designed to assist farmers in detecting crop diseases early by providing a platform where users can upload images of crop leaves such as banana, corn, mango, potato, and tomato. The system automatically analyzes the images using a Convolutional Neural Network (CNN) model to identify possible diseases, provide treatment suggestions, and generate confidence scores. Farmers can also access their diagnostic history, view data analytics on crop health, and participate in community forums for agricultural knowledge sharing. For administrators and researchers, the system stores and organizes agricultural data, allowing better monitoring of disease trends and supporting decision-making for improved crop management. Once implemented, the system benefit farmers by giving them fast and reliable diagnoses without requiring expert consultation, while also aiding agricultural experts and researchers through data-driven insights.
The system is built using the following development tools such as: Python with TensorFlow/Keras for building and training the CNN model, PHP and JavaScript for web development, CSS and Tailwind CSS for styling and responsive layout, and MySQL/MariaDB as the database management system. The frontend integrates Chart.js for visualizing disease frequency, confidence scores, and trend data. The mobile interface uses frameworks compatible with Android/iOS to ensure accessibility for rural farmers. To guarantee quality and effectiveness, the system is evaluated using the ISO/IEC 25010 software quality model, which measures functionality, usability, reliability, performance efficiency, and maintainability. This ensures that AgroAI is not only accurate and user-friendly but also reliable, efficient, and practical for addressing the real-world needs of farmers and agricultural communities.
The study followed an applied research approach using the Iterative System Development Life Cycle (SDLC) model. The system is evaluated using the ISO/IEC 25010 Software Quality Model focusing on functionality, usability, reliability, performance efficiency, and maintainability. Fifty (50) respondents participated in the evaluation activities. Results showed that the system performed well across all quality characteristics, with both groups “Strongly Agreeing” that AgroAI is reliable, efficient, and user-friendly.
The findings suggest that AgroAI is a practical, accurate, and effective solution for early crop disease detection. The researchers recommend future enhancements such as offline access, an expanded crop database, and integration of IoT devices for real-time monitoring.