AC-UNet: an improved UNet-based method for stem and leaf segmentation in Betula luminifera

被引:5
作者
Yi, Xiaomei [1 ]
Wang, Jiaoping [1 ]
Wu, Peng [1 ]
Wang, Guoying [1 ]
Mo, Lufeng [1 ]
Lou, Xiongwei [1 ]
Liang, Hao [1 ]
Huang, Huahong [1 ]
Lin, Erpei [1 ]
Maponde, Brian Tapiwanashe [1 ]
Lv, Chaihui [2 ]
机构
[1] Zhejiang A&F Univ, Coll Math & Comp Sci, Hangzhou, Peoples R China
[2] Hangzhou Ganzhi Technol Co Ltd, Hangzhou, Peoples R China
来源
FRONTIERS IN PLANT SCIENCE | 2023年 / 14卷
关键词
Betula luminifera; stem and leaf division; UNET; hollow space pyramidal pooling; crossed attention;
D O I
10.3389/fpls.2023.1268098
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.
引用
收藏
页数:16
相关论文
共 34 条
  • [21] Segmentation of brain tumor MRI image based on improved attention module Unet network
    Zhang, Lei
    Lan, Chaofeng
    Fu, Lirong
    Mao, Xiuhuan
    Zhang, Meng
    SIGNAL IMAGE AND VIDEO PROCESSING, 2023, 17 (05) : 2277 - 2285
  • [22] A Semi-Supervised Video Object Segmentation Method Based on ConvNext and Unet
    Han, Dan
    Xiao, Yuelei
    Zhan, Pengyu
    Li, Tao
    Fan, Mengyu
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 7425 - 7431
  • [23] EA-UNet Based Segmentation Method for OCT Image of Uterine Cavity
    Xiao, Zhang
    Du, Meng
    Liu, Junjie
    Sun, Erjie
    Zhang, Jinke
    Gong, Xiaojing
    Chen, Zhiyi
    PHOTONICS, 2023, 10 (01)
  • [24] Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
    Yu, Fan
    Li, Kailang
    Zhang, Hua
    Zhang, Rui
    Gao, Zhang
    Huang, Yubin
    KSCE JOURNAL OF CIVIL ENGINEERING, 2023, 27 (11) : 4834 - 4848
  • [25] Pore Structure Identification Method for Pervious Concrete Based on Improved UNet and Fusion Algorithm
    Fan Yu
    Kailang Li
    Hua Zhang
    Rui Zhang
    Zhang Gao
    Yubin Huang
    KSCE Journal of Civil Engineering, 2023, 27 : 4834 - 4848
  • [26] Dfp-Unet: A Biomedical Image Segmentation Method Based on Deformable Convolution and Feature Pyramid
    Yang, Zengzhi
    Wei, Yubin
    Yu, Xiao
    Guan, Jinting
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PT IV, PAKDD 2024, 2024, 14648 : 297 - 309
  • [27] A Semantic Segmentation Method Based on AS-Unet plus plus for Power Remote Sensing of Images
    Nan, Guojun
    Li, Haorui
    Du, Haibo
    Liu, Zhuo
    Wang, Min
    Xu, Shuiqing
    SENSORS, 2024, 24 (01)
  • [28] CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images
    Ye, Huanran
    Liu, Sheng
    Jin, Kun
    Cheng, Haohao
    2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 166 - 172
  • [29] Water body segmentation in remote sensing images based on multi-scale fusion attention module improved UNet
    Shi, Tian-Tan
    Guo, Zhong-Hua
    Yan, Xiang
    Wei, Shi-Qin
    CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS, 2023, 38 (03) : 397 - 408
  • [30] An improved UNet model based on adaptive activation function and squeeze-and-excitation module for milling tool wear segmentation
    Cai, Canyu
    You, Zhichao
    Li, Changgen
    Sun, Yi
    Li, Shichao
    Gao, Hongli
    2023 PROGNOSTICS AND HEALTH MANAGEMENT CONFERENCE, PHM, 2023, : 272 - 277