Segmentation of field grape bunches via an improved pyramid scene parsing network

Shan Chen, Yuyang Song, Jinya Su, Yulin Fang, Lei Shen, Zhiwen Mi, Baofeng Su*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
4 Downloads (Pure)


With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots.

Original languageEnglish
Pages (from-to)185-194
Number of pages10
JournalInternational journal of agricultural and biological engineering
Issue number6
Publication statusPublished - 1 Nov 2021

Bibliographical note

Acknowledgements: This study was financially supported by the Key R&D Project of Ningxia Hui Autonomous Region (Grant No. 2019BBF02013) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001). The authors would like to acknowledge Yixue Liu and Yulin Liu for their help in field data collection and the support of the staff at the experimental site.


  • grape bunches
  • semantic segmentation
  • deep learning
  • improved PSPNet


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