Abstract: In order to solve the problem that more complex backgrounds such as weeds and shadows affect the accuracy of pear canopy image information extraction, a pear canopy image segmentation method based on improved DeepLabV3+ is proposed. This method introduces the attention mechanism into the backbone network of the DeepLabV3+ encoding part, between the hole space pyramid pooling module and the backbone network of the decoding part. After that, the important feature information will be paid attention to, which improves the model segmentation accuracy and ensures the segmentation efficiency. Taking the pear orchard with Y-shaped trellis as the test object, the canopy segmentation experiment was carried out by collecting the canopy photos of pear trees by UAV. The results showed that the average intersection ratio, category average pixel accuracy and accuracy of the CBAM-DeepLabV3+ model proposed in this paper for pear canopy image segmentation were 88.72%, 94.56% and 96.65%, respectively, and the segmentation time of a single image was 0.107 s. Compared with DeepLabV3+ and Se DeepLabV3+, the classification average pixel accuracy of CBAM-DeepLabV3+ model for pear canopy segmentation was improved by 2.28% and 0.56%, respectively.