Abstract: In order to achieve accurate instance segmentation and leaf age identification of weeds and maize in a complex field environment, a plant leaf age segmentation method based on an improved Mask Regions with convolutional neural network features（Mask R-CNN） was proposed. The specific implementation was to construct a data set containing different weather（sunny, cloudy, rainy） and different acquisition angles（top view, 30° squint, 45° squint）, and to enhance the data for network input.In order to improve the accuracy of the model, the following methods were used, such as replacing three feature extraction networks（ResNet-50, ResNet-101, MobileNetv2）, building a variety of different sizes of regional suggestion boxes, replacing the Soft-NMS algorithm with a non-maximum suppression algorithm（Non-maximum suppression, NMS）, and using RoIAlign instead of the traditional RoI Pooling.Tested weeds and maize images in a complex field environment, the results showed that the improved deep learning model with ResNet-101 as the feature extraction network had good segmentation performance and robustness, the detection accuracy of cloudy was higher than that of sunny and rainy,and the detection effect of 30° squint was better than 45° squint and top view angle. The AP50 of this segmentation model was 0.730, which was higher than the accuracy of the existing DeepMask, MNC,and Mask R-CNN segmentation models, indicating that this method could more accurately perform instance segmentation and leaf age recognition for weeds and maize plants.