Authors - Pitchayapatchaya Srikram, Thanapak Khattiya, Pathompong Charoansrimuang, Chayanit Yoosri, Nachirat Rachburee Abstract - Chalkiness in Thai Hom Mali rice is not only an important quality attribute for their market value and consumer acceptance, but also for rice grain breeding. However, conventional chalkiness evaluation relies on manual inspection, which is subjective and time-consuming. This study proposes an automatic multi-level chalkiness analysis framework based on semantic segmentation using a U-Net architecture with a ResNet34 encoder to segment rice grains and chalky regions from digital images. Then it estimates the grain counts for pixel-level segmented rice regions and chalky regions to classify chalkiness levels. We compare experimental results across datasets with and without the black background label. Both results are not significantly different in loss value, Mean IoU, Dice score, and F1 score. From a practical perspective, the segmentation of both datasets differs between rice and chalky regions due to illumination. The dataset, including the black background label, shows clearer chalky-grain segmentation regions and is closer to the ground truth. In contrast, the dataset excluding the background label shows chalky-grain segmentation regions and is closer to the original image.
