4.5 Article

Computer Vision for High-Throughput Quantitative Phenotyping: A Case Study of Grapevine Downy Mildew Sporulation and Leaf Trichomes

Journal

PHYTOPATHOLOGY
Volume 107, Issue 12, Pages 1549-1555

Publisher

AMER PHYTOPATHOLOGICAL SOC
DOI: 10.1094/PHYTO-04-17-0137-R

Keywords

-

Categories

Funding

  1. United States Department of Agriculture-National Institute for Food and Agriculture Specialty Crop Research Initiative [2011-51181-30635]
  2. New York Wine & Grape Foundation
  3. Lake Erie Regional Grape Processor's Fund
  4. Charles R. Bullis Plant Hybridization Endowment
  5. Michael Nolan Endowment Fund
  6. Federal Capacity Funds
  7. NIFA [2011-51181-30635, 579463] Funding Source: Federal RePORTER

Ask authors/readers for more resources

Quantitative phenotyping of downy mildew sporulation is frequently used in plant breeding and genetic studies, as well as in studies focused on pathogen biology such as chemical efficacy trials. In these scenarios, phenotyping a large number of genotypes or treatments can be advantageous but is often limited by time and cost. We present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient. The pipeline was tested on images from leaf disc assay experiments involving two F-1 grapevine families, one that had glabrous leaves (Vitis rupestris B38 x 'Horizon' [RH]) and another that had leaf trichomes (Horizon x V. cinerea B9 [HC]). Correlations between computer vision and manual visual ratings reached 0.89 in the RH family and 0.43 in the HC family. Additionally, we were able to use the computer vision system prior to sporulation to measure the percent leaf trichome area. We estimate that an experienced rater scoring sporulation would spend at least 90% less time using the computer vision system compared with the manual visual method. This will allow more treatments to be phenotyped in order to better understand the genetic architecture of downy mildew resistance and of leaf trichome density. We anticipate that this computer vision system will find applications in other pathosystems or traits where responses can be imaged with sufficient contrast from the background.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available