Development of cost-effective arduino-based object detection and color sorting with conveyor system for experiential learning in automation and digitalization - Mat Saat, Ezril Hisham; Abdul Rahman, Noor Hasliza; Rifin, Rozi; Mokhtar, Siti Musliha Ajmal (Universiti Teknologi MARA Cawangan Johor Kampus Pasir Gudang, 2025)
This study presents the design and development of a cost-effective, DIY Arduino-based object detection system integrated with conveyor and actuator components, aimed at enhancing educational applications in automation and digitalization. Addressing the high costs and complexity of traditional industrial automation systems, the proposed solution leverages the accessibility and flexibility of Arduino platforms to provide an affordable and scalable tool for experiential learning. The system features an AI-driven object detection module, real-time data communication, and actuator control, simulating core functionalities of industrial setups in a classroom-friendly environment. The innovation lies in its adaptability to dynamic real-world conditions, such as variable conveyor speeds and partial object occlusions, offering students a realistic hands-on learning experience. By bridging the gap between theoretical concepts and industrial practices, this system prepares learners for the demands of Industry 4.0 while fostering technological literacy. The solution's affordability and modular design make it highly replicable for institutions with limited resources, extending its socio-economic impact by democratizing access to cutting-edge educational technologies. With potential applications in vocational training and STEM education, this system holds promise for commercialization as an educational toolkit, offering unique value to schools, training centers, and educational product developers seeking cost-efficient automation teaching aids