This paper presents a study on the analysis and ranking of informative color characteristics for the recognition of maize kernels infected with Fusarium moniliforme. Digital images of healthy and infected kernels from several maize varieties were used to extract 17 color features from the RGB, HSV, Lab, YCbCr, XYZ, and xyY color models. The study aimed to determine the most informative descriptors for distinguishing infected kernels from healthy ones and to evaluate their classification performance using a Support Vector Machine with a radial basis function kernel. The results demonstrated that the informativeness of color features depends on the maize variety, while several descriptors showed consistently high discriminative potential. Among them, the S component of the HSV color model was identified as the most stable and informative feature for a number of varieties. The proposed approach achieved classification accuracy above 95% for “Uzbekistan 601 ESV,” “Uzbekistan 300 MV,” and “Moldavskiy 257 SV,” confirming the feasibility of color-based machine vision for automated detection of Fusarium-infected maize kernels. The findings indicate that feature ranking makes it possible to identify compact and effective subsets of descriptors, thereby improving recognition efficiency and reducing computational cost. The developed approach may be applied in intelligent grain inspection and sorting systems for agricultural quality control.