The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapaL. ssp.pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. TheR2of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, theR2of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.