DESIGN AND VALIDATION OF A LOW-COST AI-BASED RECYCLABLE WASTE CLASSIFICATION SYSTEM
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Abstract
This paper presents the design and validation of a low-cost AI-based Smart Electronics Classification System for recyclable waste sorting. It was developed as a low-cost technical prototype that can be independently assembled using accessible components and manually collected training data, with future application as a STEM learning tool to be evaluated in subsequent work. The system uses a DC motor-driven rotating the three waste bins positioned at 120°, 240°, and 360°, controlled by Arduino Uno microcontroller combined with a result of an image-based classifier trained by PictoBlox. Images were classified into four classes: Class A (clear PET with label and cap), Class B (clear PET without label or cap), Class C (HDPE with label and cap), and Class D (empty tray). The 1,496 images dataset was manually collected, labelled and trained using PictoBlox’s built-in image learning feature by achieving 100% accuracy on the images test set. Motor positioning validation was tested over 15 trials with result of a mean absolute error of 17.0°, while end-to-end system testing over 56 trials achieved an overall system accuracy of 96.4%. The validated prototype demonstrates technical feasibility and reliability, establishing a foundation for future STEM-based learning activities. It offers a practical platform for students to engage with machine learning, embedded systems, engineering design, and problem-solving through hands-on construction and testing.
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