Currently grassland environment is complex, pastures are scattered and have little difference in color from the background. It isn't achieved the efficient and accurate segmentation. Therefore, this paper proposes a novel lightweight and multi-scale DeeplabV3+ network (LMS-DeeplabV3+). The network uses DeeplabV3+ as the base network, and first selects the lightweight MobilenetV2 as the backbone network for initial feature extraction and adjust its configuration to suit the pasture segmentation task; secondly the depth separable convolution is used instead of normal convolution in both the enhanced feature extraction and decoding modules to lighten the network; in addition, the dense atrous spatial pyramid pooling (DASPP) module is used to capture a larger sensory field and enhance the interaction among features; the convolutional block attention module (CBAM) is also introduced to reassign weights to enhance feature extraction. Experiments show that the proposed new network improves mean intersection over union (mIOU) by 8.06 percentage points and mean pixel accuracy (mPA) by 6.75 percentage points compared with the original network, reduces both the computation and the number of parameters of network by more than 90%, improves the segmentation prediction speed, and performes better in all aspects compared with other mainstream segmentation networks.