A low-cost machine-vision inspection system for RSS1--RSS5 rubber sheet grading using standardised illumination and HSV representation

Manual grading of Ribbed Smoked Sheets (RSS) is labour-intensive and experience-dependent, which can lead to inconsistent grade assignment and reduced transparency in quality evaluation. This paper presents a low-cost machine-vision inspection system for full-spectrum RSS grading (RSS1–RSS5) that integrates standardised image acquisition and HSV-based colour representation with a lightweight convolutional neural network (CNN) classifier. Images were captured using a custom-built acquisition setup with fixed illumination and camera geometry to improve repeatability across samples. To enhance grading-relevant cue representation, RGB images were transformed into the Hue–Saturation–Value (HSV) colour space, separating chromatic information from brightness prior to classification. Using a balanced dataset of expert-labelled images, the HSV-based system achieved 91.87% classification accuracy, outperforming an RGB baseline (87.81%). Confusion-matrix analysis indicated strong performance across all five grades, with remaining errors concentrated between visually similar adjacent grades. The results demonstrate the feasibility of combining low-cost standardised illumination with HSV-based colour processing and lightweight learning for practical RSS inspection and grading.