Agri-Scan: Mobile-based Rice Crop Health Detection and Monitoring Using Artificial Intelligence

Rice remains one of the most important agricultural products in the Philippines, yet many small-scale farmers in Alaminos City, Pangasinan still face difficulties in monitoring rice crop health due to limited access to digital agricultural tools. Globally, rice production has continued to increase, with Asia accounting for the majority of the world's total output, and the Philippines is among the top rice producing countries (FAO, 2023; USDA, 2024). Despite this, crop diseases left undetected can reduce yield by a significant margin (Savary et al., 2019). This study aimed to develop a mobile-based rice crop disease detection system with a user-friendly interface to help farmers and agriculturists monitor crop conditions more efficiently. Data were gathered from 30 farmers, 30 agriculturists, and 2 IT experts through interviews, observations, and surveys to identify the current process of crop health management as well as the gaps and limitations of existing tools. The study utilized the Rapid Application Development (RAD) model in designing and developing the application, which integrates a YOLO11 computer vision model trained through Roboflow to detect rice crop diseases through image analysis. The developed system, Agri-Scan, includes a disease detector, scan log, geolocation, a Palay Diseases reference guide, a weather information module with an AI disease advisor, and a farming guide. The application was evaluated by 30 respondents from the Tangcarang Tech Demo Farm of the Agricultural Office of Alaminos City using the ISO/IEC 25010 software quality model. Results show that Agri-Scan obtained an overall weighted mean of 4.1, described as Very Good, with Usability rated Excellent at 4.3. Findings further revealed that traditional crop health management remains manual, experience based, and reactive, and that existing digital tools remain largely inaccessible to small-scale farmers. The study concludes that integrating a user-friendly, AI-powered mobile application into agriculture can improve crop monitoring and support more sustainable farming practices in Alaminos City.

RICE CROP DISEASE DETECTION, DIGITAL AGRICULTURE

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