Development of a Cost-Effective Artificial Intelligence-Based Image Processing Sorting Mechanism for Conveyor Belt System

Integration of artificial intelligence in industrial automation has led to significant advancements in new techniques for automation. Such an aspect of industrial automation includes sorting consumables on conveyor belt systems via image processing. Typically, these applications use expensive dedicated, and focus-driven hardware and individual image-processing coding. This paper discusses the development of such an imageprocessing sorting conveyor belt but utilizing low-cost processors compared to dedicated and focus-driven hardware. This is achieved by using at the core of this system a Convolutional Neural Network (CNN), specifically tailored for hue-based image processing, and implemented on a Raspberry Pi 4B. A standard Pi camera, attached to the Raspberry Pi, captures images for realtime object classification. A key innovation of the system is the utilization of a pixel-based trigger mechanism for image capture, which significantly improves the accuracy and efficiency of the sorting process. The system achieves an accuracy rate of 92.74% in classifying objects as trained, underscoring the efficacy of the approach. Additionally, the system operates in a dual-mode capacity, enabling not only the sorting of existing object types but also the learning and adaptation to new objects through user input. This feature enhances the system's versatility and applicability in various industrial contexts. The paper details the design, implementation, and testing of this AI-driven sorting mechanism, highlighting its potential as a scalable and low-cost solution for modern industrial sorting needs.