Campus Units

Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy, Entomology

Document Type

Article

Publication Version

Published Version

Publication Date

7-28-2020

Journal or Book Title

Applications in Plant Sciences

Volume

8

Issue

7

First Page

e11375

DOI

10.1002/aps3.11375

Abstract

Premise Trichomes are hair‐like appendages extending from the plant epidermis. They serve many important biotic roles, including interference with herbivore movement. Characterizing the number, density, and distribution of trichomes can provide valuable insights on plant response to insect infestation and define the extent of plant defense capability. Automated trichome counting would speed up this research but poses several challenges, primarily because of the variability in coloration and the high occlusion of the trichomes.

Methods and Results We developed a simplified method for image processing for automated and semi‐automated trichome counting. We illustrate this process using 30 leaves from 10 genotypes of soybean (Glycine max ) differing in trichome abundance. We explored various heuristic image‐processing methods including thresholding and graph‐based algorithms to facilitate trichome counting. Of the two automated and two semi‐automated methods for trichome counting tested and with the help of regression analysis, the semi‐automated manually annotated trichome intersection curve method performed best, with an accuracy of close to 90% compared with the manually counted data.

Conclusions We address trichome counting challenges including occlusion by combining image processing with human intervention to propose a semi‐automated method for trichome quantification. This provides new opportunities for the rapid and automated identification and quantification of trichomes, which has applications in a wide variety of disciplines.

Comments

This is the published version of the following article: Mirnezami, Seyed Vahid, Therin Young, Teshale Assefa, Shelby Prichard, Koushik Nagasubramanian, Kulbir Sandhu, Soumik Sarkar, Sriram Sundararajan, Matt E. O’Neal, Baskar Ganapathysubramanian, and Arti Singh. "Automated trichome counting in soybean using advanced image‐processing techniques." Applications in Plant Sciences 8, no. 7 (2020): e11375. DOI: 10.1002/aps3.11375. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Mirnezami et al.

Language

en

File Format

application/pdf

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