Automatic image-based determination of pruning mass as a determinant for yield potential in grapevine management and breeding

A. Kicherer, M. Klodt, S. Sharifzadeh, D. Cremers, R. Töpfer, K. Herzog

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Background and Aims: Vine balance is defined as a relation between vegetative (mass of dormant pruning wood) and generative (yield) growth. For grapevine breeding, emphasis is usually placed on the evaluation of individual seedlings. In this study, we calculated the mass of dormant pruning wood with the assistance of an automated image-based method for estimating the pixel area of dormant pruning wood. The evaluation of digital images in combination with depth map calculation and image segmentation is a new and non-invasive tool for objective data acquisition. Methods and Results: The proposed method was tested on a set of seedlings planted at the Institute for Grapevine Breeding Geilweilerhof, Germany. All images taken in the field were geo-referenced, and the automated method was validated by manual segmentation. Together with additional yield parameters, the vine balance indices can be used to classify seedlings for breeding purposes. Conclusion: The computed pruning mass obtained using image-based methods is an accurate, inexpensive and easy method to estimate pruning mass compared with the manual time-consuming measurements. Together with the yield parameters, it is a suitable method for seedling evaluation and can also be used in precision viticulture. Significance of the Study: This study demonstrates an image-based evaluation of the pruning mass to be a highly valuable tool for grapevine research and grapevine breeding. Moreover, the tool might be used by industry to monitor vine balance. The key findings reported have the potential to increase grapevine breeding efficiency by using an accurate and objective phenotyping method.

Original languageEnglish
Pages (from-to)120-124
Number of pages5
JournalAustralian Journal of Grape and Wine Research
Volume23
Issue number1
DOIs
StatePublished - 1 Feb 2017

Keywords

  • Vitis vinifera
  • automated image segmentation
  • computed pruning mass
  • dense depth map
  • grapevine breeding
  • vine balance

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