Wang, D.; Huang, Z.; Yuan, H.; Liang, Y.; Tu, S.; Yang, C. Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation. Agriculture2023, 13, 1662.
Wang, D.; Huang, Z.; Yuan, H.; Liang, Y.; Tu, S.; Yang, C. Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation. Agriculture 2023, 13, 1662.
Wang, D.; Huang, Z.; Yuan, H.; Liang, Y.; Tu, S.; Yang, C. Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation. Agriculture2023, 13, 1662.
Wang, D.; Huang, Z.; Yuan, H.; Liang, Y.; Tu, S.; Yang, C. Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation. Agriculture 2023, 13, 1662.
Abstract
Plant phenotype plays an important role in crop breeding and planting. Leaf phenotype is an important part of plant phenotype. In order to analyze the leaf phenotype, the target leaf is required to be segmented from the complex background image. In this paper, an automatic soybean leaf segmentation method based on object detection and interactive segmentation models is proposed. Firstly, the Libra R-CNN object detection algorithm is used to detect all soybean leaves in the image. Then, based on the idea that the target soybean leaf is located in the center of the image and the area is large, the detection bounding box of the target leaf is selected. In order not to destroy the segmentation result, the bounding box is optimized to completely enclose the whole leaf. Finally, according to the optimized bounding box, the prior channels of foreground and background are constructed using Gaussian model. The two channels together with the original image are as the input of the interactive object segmentation with inside-outside guidance model to segment the target soybean leaf. A large number of qualitative and quantitative experimental results show that the method has high segmentation accuracy and strong generalization capacity.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
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