To improve the effect of plant segmentation and achieve accurate acquisition of plant phenotypic parameters, this study proposes a plant segmentation network to fuse spatial-frequency domain feature representation. Based on the U-Net network architecture, the frequency domain transform module is built for down-sampling, the frequency domain feature representation is introduced in the convolutional neural network to replace the pooling layer, and the frequency domain transform method of 2D-DCT and 2D-IDCT is used to perceive the global semantic features of the plant. The multi-scale feature fusion module is constructed by adding six up-sampling nodes to extract and connect the fine-grained feature information of the plant image. The channel attention module is modified to learn branch features and the hybrid loss function is employed to optimize the network. Experiments are conducted on the 2017 CVPPP public dataset, and the results show that the intersection over union, mean intersection over union, pixel accuracy, precision and F1 of the plant segmentation network reach 97.07%, 98.04%, 99.53%, 99.68% and 99.74%, respectively. Compared with the FCN-8s, FCN-ResNet, DeepLabV3+, SegNet and U-Net models, the intersection over union and mean intersection over union of the network were improved by up to 23.32% and 12.43%. The proposed method can improve the segmentation accuracy of plant at smaller scales in detail processing, and it can provide a useful idea for applied research in the plant phenotype.