A Quantitative Descriptor for Chalky and Discoloration Damage in Rice Grains

Abstract

The quality assessment of chalky and discolored rice grains is often limited to either subjective methods or single‐value metrics that fail to capture the finer details of these damages. Despite being detectable and rich in visual features, there is a lack of a quantitative framework for characterizing these damages within a single rice grain in terms of their severity, their precise location, and their dispersion patterns across the grain surface. To address this gap, a multifeature‐based computer vision framework is proposed on a dataset of 5598 high‐resolution (24 megapixels) and high‐magnification (3.9 μm/pixel) images. The framework involves a two‐stage process: first, the unsupervised segmentation of the images via K ‐means clustering; and second, the spatial, geometric, and intensit