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Segmentation and 3D reconstruction of rose plants from stereoscopic images

Research output: Contribution to journalArticle

  • Hanz Cuevas Velasquez
  • Antonio Javier Gallego Sánchez
  • Bob Fisher

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Original languageEnglish
Article number105296
Number of pages18
JournalComputers and electronics in agriculture
Early online date28 Feb 2020
Publication statusPublished - 30 Apr 2020


The method proposed in this paper is part of the vision module of a garden robot capable of navigating towards rose bushes and clip them according to a set of pruning rules. The method isresponsible for performing the segmentation of the branches and recovering their morphology in 3D. The obtained reconstruction allows the manipulator of the robot to select the candidate branches to be pruned. This method first obtains a stereo pair of images and calculates the disparity image using block matching and the segmentation of the branches using a Fully Convolutional Neuronal Network modified to return a map with the probability at the pixel level of the presence of a branch. A post-processing step combines the segmentation and the disparity in order to improve the results. Then, the skeleton of the plant and the branching structure are calculated, and finally, the 3D reconstruction is obtained. The proposed approach is evaluated with five different datasets, three of them compiled by the authors and two from the state of the art, including indoor and outdoor scenes with uncontrolled environments. The different steps of the proposed pipeline are evaluated and compared with other state-of-the-art methods, showing that the accuracy of the segmentation improves other methods for this task, even with variable lighting, and also that the skeletonization and the reconstruction processes obtain robust results.

    Research areas

  • Computer vision, Stereo vision, Semantic segmentation, 3D modelling, Automated agriculture

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