Comparative Study of Red-Green-Blue (RGB), Hue-Saturation-Value (HSV), CIE L*a*B* (Lab), and Luminance-Chrominance (YCbCr) Colour Spaces for Fruit Segmentation in Agricultural Images

Fruit detection and segmentation are key components of agricultural automation pipelines, enabling tasks such as yield estimation, quality sorting, and robotic harvesting in resource-constrained environments. This study evaluates four colour spaces – RGB, HSV, CIE L*a*b* (LAB), and YCbCr – for threshold-based fruit segmentation in precision agriculture and robotic harvesting. It specifically investigates which colour space maintains the highest accuracy and stability under variable indoor illumination. A dataset of 144 images featuring fruits from four colour categories (red, green, orange, and yellow) was utilised. Colour thresholds were calibrated through manual region-of-interest selection and applied using chromatic thresholding and morphological refinement, resulting in 2304 mask evaluations based on intersection over union (IoU), precision, recall, and F1-score. Clear and statistically significant differences were found among colour spaces: HSV achieved the highest overall performance (F1-score = 0.972, IoU = 0.947), followed by RGB (F1-score = 0.823), LAB (F1-score = 0.714), and YCbCr (F1-score = 0.682), confirmed by non-parametric statistical testing. HSV is established as the most accurate and consistent colour representation for fruit segmentation under the proposed experimental protocol, providing practical guidance for lightweight, real-time segmentation pipelines in agricultural automation systems operating under variable illumination conditions.