Fruit Intelliripen Embedded System

The rising need for fast, dependable, and easily accessible fruit-quality evaluation solutions has increased the demand for digital systems that help consumers, farmers, and vendors assess the condition of fruits in real time. Conventional inspection methods often depend on visual estimation, manual handling, or outdated storage records to detect wax coating, stages of ripeness, or early signs of spoilage. Such limitations become even more critical in large- scale distribution environments, where timely quality assessment directly affects freshness, safety, and overall market value. This work introduces Fruit Intelliripen, an embedded smart produce-analysis system developed to deliver real-time fruit-quality data, including wax identification, freshness prediction, ripeness classification, and IoT-based maturity monitoring. The platform combines camera-driven image analysis, machine-learning models, QR-based traceability, and sensor-assisted data collection to provide precise and continuously updated fruit-quality insights. Users can analyze fruits through live detection or uploaded photos, verify ripeness levels using IoT sensors, and access batch information through QR scanning, enabling quicker and more informed decisions. Farmers, sellers, and storage operators continuously update their dash boards, supporting improved oversight throughout the supply chain. Comprehensive testing shows that Fruit Intelliripen delivers low-latency recognition, scales efficiently, and minimizes errors in identifying waxed, spoiled, or unripe produce. Overall, the system functions as a practical smart-quality assessment tool that enhances safety, transparency, and operational efficiency within today’s agricultural and retail ecosystems.