Counting of grapevine berries in images via semantic segmentation using convolutional neural networks

Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.

Original languageEnglish
Pages (from-to)73-83
Number of pages11
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Jun 2020
Externally publishedYes


  • Deep learning
  • Geoinformation
  • High-throughput analysis
  • Plant phenotyping
  • Semantic segmentation
  • Vitis


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