Addressing difficulties in manual measurement and statistics of key parameters like the number and area of vascular bundles in crop stem microsection images such as high subjectivity, large time, labor investment, and low efficiency, an automatic detection method of rice stem cross-section parameters based on image processing was proposed. First of all, an image segmentation model of rice stem slices based on the improved Mask R-CNN was built. The network adopted MobilenetV2 and residual feature enhancement and the adaptive space fusion feature pyramid network as the feature extraction network. In the meantime, the PointRend enhancement module was introduced, and the regression loss function of the network was optimized to IoU function. The F1 value of the optimal model was 91.21%; the average precision rate was 94.37%; the recall rate was 88.25%; the mean intersection over union was 90.80%; and the average detection time of a single image was 0.50s. It achieved localization, detection and segmentation of large and small vascular bundle areas in rice stem slice images. Through edge detection, morphological processing and contour extraction, the stem section contours were segmented and extracted. The method proposed herein realized automatic detection of six parameters, namely rice stem section area, section diameter, large and small vascular bundle area, and the number of large and small vascular bundles. The average relative error of detection was no higher than 4.6%. The method can also be used for high-throughput observation of rice stem microstructure.