Conference Agenda

Small-scale rice farmers in resource-constrained rural regions experience persistent productivity losses due to limited access to advanced agricultural technologies for pest and disease management. This paper presents a work-in-progress study on the development of an offline software system integrating Augmented Reality (AR) and Artificial Intelligence (AI) to support real-time detection of pests and diseases directly in the field. The proposed system combines computer-vision-based detection, an AI-driven chatbot, and AR visualization through low-cost AR glasses, enabling farmers to receive actionable recommendations without continuous internet connectivity. The project follows a phased methodology encompassing data collection, prototype development, field testing, and system refinement. At the current stage, a balanced dataset of 2,000 rice images captured using Vuzix Blade 2 AR glasses has been annotated and used to train and test a YOLO11n-based object detection model in a desktop environment, producing preliminary qualitative detection and localization outputs. This work aims to improve crop management efficiency, reduce chemical inputs, and enhance agricultural sustainability, while establishing a foundation for future quantitative evaluation and large-scale deployment.