d the threshold and Hough circle transform algorithm parameters and lines.py is used to find the Hough line transform algorithm parameters. All of the scripts are parallelized, such that, when run, they automatically use all available CPU cores for faster image processing. The scripts and a guide for the scripts can be found at https://github.com/kdivilov/downymildew-CV.For the computer vision system, the images were initially cropped so that only leaf discs that were fully contained in an image were kept (Fig. 1). The cropped images were then converted to Lab color space, which, unlike RGB color space, includes all colors visible to the human eye, with all of the layers thresholded using us
Open resource ↗kdivilov/downymildew-CV · pdf-raw-page:2 lines:79-134Unverified paper record
Computer Vision for High-Throughput Quantitative Phenotyping: A Case Study of Grapevine Downy Mildew Sporulation and Leaf Trichomes
Phytopathology® · 1 Dec 2017 · 10.1094/phyto-04-17-0137-r
Abstract
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 × ‘Horizon’ [RH]) and another that had leaf trichomes (Horizon × 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.
Plant phenotyping relevance
画像からブドウ葉のべと病胞子形成面積と毛状突起面積を定量推定するコンピュータビジョン手法の開発・検証が中心であり、植物表現型手法に該当する。
abstractWe 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.
abstractCorrelations between computer vision and manual visual ratings reached 0.89 in the RH family and 0.43 in the HC family.
abstractAdditionally, we were able to use the computer vision system prior to sporulation to measure the percent leaf trichome area.
Code and data availability
The paper's authors publicly deposited the four Python/OpenCV scripts (crop.py, values.py, circles.py, lines.py) used to quantify downy mildew sporulation and leaf trichome area from smartphone leaf-disc images, with an explicit availability statement and URL. No phenotype dataset or image deposit is stated in the text
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