Although numerous rice genotypes have been developed worldwide, post-harvest evaluation of chalkiness, a key grain trait, remains a significant challenge in breeding programs. Conventional phenotyping methods rely on manual grain separation and analysis, which limit the speed and performance of decision-making. This study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphological traits (grain length and width) in comparison with traditional evaluation methods. Grains from 270 rice samples were imaged using a hyperspectral camera (visible to near-infrared, 400-1 000 nm) and a Nikon digital single-lens reflex (DSLR) camera. Only RGB information was used for analysis, including RGB channels extracted from hyperspectral imagery to simulate low-cost setups. Python scripts were used to segment grains, estimate morphological parameters, and calculate chalkiness degree. Results from both imaging systems were compared with reference data obtained from the SeedCount platform. Strong correlations with SeedCount reference data were observed for chalkiness degree, reaching 93% when using hyperspectral-derived RGB data and 76% when using DSLR-acquired RGB images. Binary classification metrics showed high discriminative performance, with area under the curve (AUC) values above 0.90 for most traits. The proposed method enabled image acquisition and processing in approximately 21 s per sample, compared to 1.5 min required by the conventional platform. The findings demonstrate the feasibility of a rapid and low-cost image-based phenotyping strategy to support rice breeding programs, particularly for chalkiness quantification and grain morphology assessment. The complete image-processing pipeline is provided as supplementary material, reinforcing the transparency and reproducibility of the method.