English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We developed a simple and inexpensive digital image analysis method for measuring leaf area and the length of strapped-shaped seagrass leaves. The method uses specialized software that permits automatic leaf and scale recognition in digital images along with batch image processing. The leaf biometrics of 80  Posidonia oceanica  and  Cymodocea nodosa  leaves were used for the evaluation of the method for accuracy, precision, and speed in comparison to manual measurements. Each leaf was measured 90 times under different combinations of several factors that might affect software estimation. The factors examined included leaf shape, leaf orientation in the image, scale dimensions, image resize, and binary conversion. Estimates of leaf length and leaf area were highly correlated between the new image analysis procedure and manual measurements. Relative error values between the two methods were &lt;10% in 99% of leaf length measures and in 75% of leaf area measures. The factors examined explained &lt;10% of the relative error variance; the remaining variance was mostly attributed to technical issues concerning leaf size and image illumination. The digital method proved to be approximately 2.5 times faster than the manual one.

Development and evaluation of an automated digital image analysis software for obtaining seagrass leaf metrics