A non-destructive UV-based imaging system designed to enable early detection and classification of fruit damage (proof of concept performed with navel orange). The system integrates machine learning (YOLOv5) to automatically differentiate healthy and defective fruits in real time. By combining AI-powered image recognition and IoT connectivity, this solution delivers accessible, cost-effective, and scalable fruit quality assessment for small and medium farms across ASEAN.

This product supports sustainable agriculture by reducing post-harvest losses and promoting efficient quality control. It empowers farmers with affordable AI tools to improve food safety, minimize waste, and strengthen supply chain transparency. By reducing unnecessary fruit waste and enabling precise monitoring, the system contributes to green technology adoption. It aligns with ASEAN goals for inclusive digital transformation in agriculture and supports several Sustainable Development Goals (SDGs), including: SDG 3 – Good Health & Well-being, SDG 9 – Industry, Innovation, and Infrastructure and SDG 12 – Responsible Consumption and Production