Back to the Basics: Birmingham, Alabama, Measurement and Scale

Handley, Lawrence R.; Lockwood, Catherine M.; Handley, Nathan

doi:10.1080/00221340508978988

Journal of Geography

2005

DOI: 10.1080/00221340508978988

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Back to the Basics: Birmingham, Alabama, Measurement and Scale

Lawrence R. Handley¹,

Catherine M. Lockwood²,

Nathan Handley³

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2020

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Trichomes are hair-like appendages extending from the plant epidermis (Levin, 1973). They serve many important biotic roles for plants, including interference with herbivore movement, preventing herbivory, and reducing the area available for the attachment of insect eggs (Handley et al, 2005). In addition, trichomes play many other important roles, such as the protection of the plant from solar radiation, salt balance in the form of excretion, seed dispersal, and reduced evaporation (Ghorashy et al, 1971;Baldocchi et al, 1983;Serna and Martin, 2006;Price et al, 2011).

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Automated trichome counting in soybean using advanced image‐processing techniques

et al. 2020

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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.

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Automated trichome counting in soybean using advanced image‐processing techniques

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Back to the Basics: Birmingham, Alabama, Measurement and Scale

Cited by 1 publication

References 2 publications

Automated trichome counting in soybean using advanced image‐processing techniques

Automated trichome counting in soybean using advanced image‐processing techniques

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