Leaf Morphology - IC Herbarium

[acespicoli]

Leaf Morphology​

To catalog is it necessary to use same light sources and are there other environmental plasticity factors to leaf morphology? Regardless we could start to collect some examples with graph paper or measurements?
Plants 2023, 12(20), 3646; https://doi.org/10.3390/plants12203646
Hope to see BLD NLD and all between just for sake of enjoying diversity, or however you class it

 

[Raco]

Hola acespicoli
Great idea! I remember that 20 years ago I started a thread called "Leaf shape map?" at Overgrow.No success...
thx for starting this one

 

[acespicoli]

Hola acespicoli
Great idea! I remember that 20 years ago I started a thread called "Leaf shape map?" at Overgrow.No success...
thx for starting this one

Brother @Raco welcome, you have some great content for this if you would co author id be honored
Hopefully here with the ICfamily the dream is fully realized

Herbarium Specimen Details - ISB: Atlas of Florida Plants

The Atlas of Florida Plants provides a source of information for the distribution of plants within the state and taxonomic information. The website also provides access to a database and images of herbarium specimens found at the University of South Florida and other herbaria.

florida.plantatlas.usf.edu

The tease of what we may accomplish is the IC Herbarium ?
title edited

Glossary of leaf morphology - Wikipedia

en.wikipedia.org

Leaf Morphology to:​

Leaf Morphology - IC Herbarium​

 

[acespicoli]

Tissue samples for analysis were collected from the first fully expanded leaf following the development of alternating phyllotaxis, which is an indication of sexual maturity in C. sativa plants
doi: 10.3390/plants12203646

 

[acespicoli]

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities​

Manica Balant, Teresa Garnatje, Daniel Vitales, Oriane Hidalgo, Daniel H. Chitwood
First published: 17 May 2024
https://doi.org/10.1111/nph.19817

Its time to take this work a little more seriously ?
Best>>>

Conclusions​

In grapevine, leaf shape has long been utilized for variety identification. However, in the case of Cannabis, previous attempts were hindered by the variability in leaflet numbers. In this study, we present a pioneering method that successfully addresses this issue. By generating theoretical leaves with customizable leaflet counts, we can now employ high-resolution morphometric techniques to accurately classify different wild/feral and cultivated Cannabis accessions. Through the use of 3591 densely placed pseudo-landmarks, we were able to predict the accession identity with almost 74% accuracy. The method works well not only on stabilized cultivars but also on phenotypically more variable wild/feral accessions grown from seed. Unifying the number of leaflets allowed us, for the first time, to make comparisons among several leaves along the main axis, enabling us to investigate developmental changes in leaf shape and detect heterochronic mechanisms influencing the leaf shape in Cannabis. The implications of this new high-resolution method in both the cannabis industry and research extend beyond its role in determining Cannabis accessions. It also offers a promising tool for developmental studies, and for studying the correlation between leaf shape and phytochemical profiles and the sex of the plants, where lower resolution methods provided inconclusive results so far. The method presented here offers a fast, effective, robust, and low-cost tool that can aid the future classification of Cannabis germplasm. Furthermore, the use of this methodology extends beyond Cannabis and can be applied to numerous other plant species with palmate, pinnate, and lobate leaves with varying numbers of lobes and leaflets where the use of geometric morphometrics methods was not previously possible to this extent.

 

[acespicoli]

Official Petiole Pro® Blog

Petiole Pro is accelerating digital transformation resilience in agriculture, forestry & aquaculture with AI & Computer Vision

www.petiolepro.com

Petiole Pro: AI for Agriculture and Forestry

Petiole Pro is a #1 super app for growers and researchers, from seed to postharvest. It brings the power of Artificial Intelligence to your agricultural and forestry needs. Petiole Pro helps in measuring key parameters in crops and plants, providing both qualitative and quantitative data to make...

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Petiole Pro: Free AI-Powered Mobile App for Leaf Analysis and Environmental Phenotyping - AI + Environment Summit 2024​

• October 2024

DOI:10.13140/RG.2.2.25106.67522

List of scientific works with citation of Petiole mobile application We are honoured about the scientific reference to Petiole Pro and Petiole App in 95 scientific research papers. Please, find the full list of citations. Otherwise, you can download the full list in pdf. We are working on more convenient mode to demonstrate this information - apologies for inconvenience, below you can see the list of selected references:

Official Petiole Pro® Blog

Petiole Pro is accelerating digital transformation resilience in agriculture, forestry & aquaculture with AI & Computer Vision

www.petiolepro.com

 

[acespicoli]

Leaf Area Calculator

Got into this a little more this is the web version, works with your web cam
There are some calibration plates that ensure accuracy of measurements, free download

Official Petiole Pro® Blog

Petiole Pro is accelerating digital transformation resilience in agriculture, forestry & aquaculture with AI & Computer Vision

www.petiolepro.com

^^^ The Cannabis Files ^^^

 

[acespicoli]

Download

imagej.net

• README

Note:​

Please install ImageJ from http://imagej.nih.gov/ij/. ImageJ2 at http://imagej.net is not supported.

LeafArea​

The package LeafArea allows one to conveniently run ImageJ software within R. The package provides a user-friendly, automated tool for measuring leaf area from digital images. For more information on ImageJ, see the ImageJ User Guide, which is available http://imagej.nih.gov/ij/.
The ImageJ function run.ij computes the total area of all leaves (or leaf sections) in each image file in the target directory. Original leaf images are converted to black and white from threshold intensity levels, then leaf area is calculated by using leaf pixel counts and the calibration scale. The user can determine if the analyzed images will be saved for error checking: run.ij (save.image = TRUE) or run.ij (save.image = FALSE).

1 Prerequisites​

From within R (>= 3.0.0), you can install:

• the latest version of LeafArea from CRAN with
  install.packages(“LeafArea”)
  
  
• the latest development version from github with
  # install.packages("devtools")
  devtools::install_github("mattocci27/LeafArea")
  

The package LeafArea requires ImageJ software, which is available from http://imagej.nih.gov/ij/. Details on how to install ImageJ on Linux, Mac OS X and Windows are available at http://imagej.nih.gov/ij/docs/install/. For Mac, the default path to ImageJ is “/Applications/ImageJ.app”. For Windows, "C:/Program Files/ImageJ. Otherwise, you need to specify the path to ImageJ to use LeafArea in R (see 3.1 Setting path to ImageJ). Note that in Linux system, ImageJ should be installed from the above URL instead of via the command lines. Java is also required, which is available at https://java.com/en/.

2 Image capture and file naming​

Capture leaf images by using a scanner and save them as jpeg or tiff files. Image size and resolution should be consistent across all the image files because the LeafArea functions estimate leaf area based on leaf pixel counts and the image size. Therefore, the LeafArea package does not support images from digital cameras, where the resolution depends on the distance of the camera to the object.
The LeafArea combines the leaf area of all images that share the same filename “prefix”, defined as the part of the filename preceding the first hyphen (-) or period (.) that may occur. For example, the areas of leaf images named A123-1.jpeg, A123-2.jpeg, and A123-3.jpeg would be combined into a single total leaf area (A123). This feature allows the user to treat multiple images as belonging to a single sample, if desired. Note that the functions in the package do not count the number of leaves in each image. If the user requires the number of leaves per image, the user must record these values by themselves.

3 How to run LeafArea​

3.1 Setting path to ImageJ​

When ImageJ is not installed in the common install directory in Linux or Windows, you need to specify the path to ImageJ in run.ij. This depends on the operating system being used (Windows, Linux or Mac). For example, when ImageJ is installed in a directory named “ImageJ” on the desktop of a Linux system, you can specify the path by typing run.ij (path.imagej = "~/Desktop/ImageJ"). Typing run.ij (path.imagej = ”C:/Users/<username>/Desktop/ImageJ”) works in Windows. For Mac, you do not have to specify the path as long as "ImageJ.app" exists in your computer.

3.2 Setting path to leaf images​

To analyze your leaf images, you need to specify the path to directory that contains leaf images. This depends on the operating system being used (Windows, Linux or Mac). For example, when the target directory named "leaf data" is on desktop of Mac or Linux, you can specify the path by typing run.ij (set.directory = "~/Desktop/leaf data/"). Typing run.ij (set.directory = "C:/Users/<username>/Desktop/leaf data") works in Windows.

3.3 Example​

This is an example in the R help. First, I use eximg function to specify the path to example leaf images in the R temporary directory. Then, I run the run.ij function which will analyze leaf area automatically:
ex.dir <- eximg()
res <- run.ij(set.directory = ex.dir)

The object ex.dir is the path to the R temporary directory that contains example leaf images. This temporary directory will be eventually deleted after the analysis. The object res, returned from LeafArea is a data frame object, which contains name of samples in the first column, total leaf area of sample (cm2) in the second column, and total perimeter of leaves (cm) in the third column.
res
#> sample total.leaf.area perimeter
#> 1 A1 350.340 174.742
#> 2 A123 418.473 172.190
#> 3 A2 177.188 154.636
#> 4 A300 384.919 186.831

4 Automated leaf area analysis​

You can change the following setting according to your images.

4.1 Spatial calibration​

You need to tell LeafArea what a pixel represents in real-world terms of distance. When leaf images are captured in A4 image size with 100 ppi, the pixel density is roughly equal to 826 pixels per 21 cm. In this case, the calibration scale can be specified as `run.ij (distance.pixel = 826, known.distance =21)``.

4.2 Memory setting​

The amount of memory available can be increased. By default, LeafArea uses 4 GB of memory. Typing run.ij (set.memory = 8) will allocate 8 GB of memory to LeafArea.

4.3 Trimming images​

The edges of images may have shadowing, which can affect image analysis (i.e., ImageJ may recognize the shaded area as leaf area). The edges of images can be removed by specifying the number of pixels (default = 20). For example, run.ij (trim.pixel = 20) will remove 20 pixels from the edges of each image.

4.4 Size and circularity​

Leaf images often contain dirt and dust. To prevent dust from affecting the image analysis, the lower limit of analyzed size can be specified. For example, typing run.ij (low.size = 0.7) will remove objects smaller than 0.7 cm2 in the analysis.

When you want to remove angular objects (e.g., cut petioles, square papers for scale) from the images, the analyzed lower limit of circularity can be increased (default = 0). For example, run.ij (low.circ = 0.3) will skip cut petioles from the analysis.

4.5 File naming​

By default, the LeafArea combines the leaf area of all images that share the same filename “prefix”, defined as the part of the filename preceding the first hyphen (-) or period (.) that may occur. You can change this setting by using regular expressions. For example, typing run.ij (prefix = ‘\\.|-|_’) will combine the area of leaf images named A123-1.jpeg, A123-2_1.jpeg, A123-2_1.jpeg into a single total leaf area (A123).

4.6 Result log​

A list object of data frames of area (cm2) and perimeter (cm) of each object in each image can be returned by typing run.ij (log = T):
ex.dir <- eximg()
run.ij(set.directory = ex.dir, log = T)

#> $summary
#> sample total.leaf.area perimeter
#> 1 A1 350.340 174.742
#> 2 A123 418.473 172.190
#> 3 A2 177.188 154.636
#> 4 A300 384.919 186.831
#>
#> $each.image
#> $each.image$`A1-01.jpeg.txt`
#> Area Perim.
#> 1 116.799 58.019
#> 2 124.069 59.092
#>
#> $each.image$`A1-02.jpeg.txt`
#> Area Perim.
#> 1 109.472 57.631
#>
#> $each.image$`A123-01.jpeg.txt`
#> Area Perim.
#> 1 184.773 71.453
#>
#> $each.image$`A123-02.jpeg.txt`
#> Area Perim.
#> 1 123.151 50.086
#> 2 110.549 50.651
#>
#> $each.image$A2.jpeg.txt
#> Area Perim.
#> 1 43.328 39.524
#> 2 47.558 41.534
#> 3 41.427 37.003
#> 4 44.875 36.575
#>
#> $each.image$`A300-1.jpeg.txt`
#> Area Perim.
#> 1 158.065 66.844
#>
#> $each.image$`A300-2.jpeg.txt`
#> Area Perim.
#> 1 124.784 62.713
#> 2 102.070 57.274

By default, run.ij returns a single data frame object, which contains name of samples in the first column, total leaf area of sample (cm2) in the second column and total perimeter of leaves of sample (cm) in the third column (see 3.0).

4.7 Saving analyzed images​

Analyzed images can be exported in the same directory as set.directory for error checking. Typing run.ij (save.image = TRUE) will export analyzed images. If you use the eximg function to set the target directory, analyzed images will be exported to a temporary directory, which will be eventually deleted. If you choose your home directory as the target directory, analyzed images will be exported to it.

4.8 Displaying analyzed images​

Analyzed image can be displayed by using ImageJ software (defalt = FALSE). When you choose run.ij (check.image = TRUE), press any keys to close ImageJ. Note that when check.image = TRUE, the analysis would take considerable time. This option may only work on R console.

5 Manual leaf area analysis​

You can skip this step if ImageJ succeeds in analyzing the leaf images. If ImageJ fails to recognize leaf images, you can manually guide the image analysis for particular images through ImageJ GUI (See the ImageJ user guide 30.1 Measure...[m], http://imagej.nih.gov/ij/docs/guide/user-guide.pdf). The results for these manually-analyzed images will still be handled by the file management function resmerge.ij in run.ij. Multiple tab-delimited text files with a leaf area value (one text file for each original JPEG image file) generated by ImageJ can be merged into a single data frame. The names of text files should be the same as the image files (e.g., A123-1.txt, A123-2.txt, A123-3.txt). For example, when the text files are on the desktop of a Mac, files can be merged using resmerge.ij(“~/Desktop”):
resmerge.ij(“~/Desktop”)
#> sample total.leaf.area
#> 1 A123 418.473

The output and the option “prefix” are same as run.ij. See ?resmerge.ij in R for a more detailed description.

 

[acespicoli]

https://imagej.net/ij/docs/guide/user-guide.pdf

ImageJ manual

References​

[1] Jadon M. A Novel Method for Leaf Area Estimation based on Hough Transform. Journal of Multimedia Processing and Technologies, Volume 9, No. 2, June 2018, https://doi.org/10.6025/jmpt/2018/9/2/33-44

[2] Pandey S., Singh H. A simple cost-effective method for leaf area estimation. Journal of Botany, 2011, https://doi.org/10.1155/2011/658240

[3] Wolf D., Carson E., Brown R. Leaf Area Index and Specific Leaf Area Determinations, 1972, https://www.crops.org/files/publications/nse/pdfs/jnr001/001-01-0024.pdf

[4] Non-destructive method for estimating leaf area of Erythroxylum pauferrense (Erythroxylaceae) from linear dimensions of leaf blades. Acta botánica mexicana, no. 127, e1717, 2020, https://doi.org/10.21829/abm127.2020.1717

[5] Chelli, S., Ottaviani, G., Simonetti, E., Campetella, G., Wellstein, C., Bartha, S., Cervellini, M. and Canullo, R. Intraspecific variability of specific leaf area fosters the persistence of understorey specialists across a light availability gradient. Plant Biol J, 23: 212-216. https://doi.org/10.1111/plb.13199 .

[6] de Ávila Silva, L., Omena-Garcia, R.P., Condori-Apfata, J.A. et al. Specific leaf area is modulated by nitrogen via changes in primary metabolism and parenchymal thickness in pepper. Planta 253, 16 (2021). https://doi.org/10.1007/s00425-020-03519-7

[7] Firn, J., McGree, J.M., Harvey, E. et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat Ecol Evol 3, 400–406 (2019). https://doi.org/10.1038/s41559-018-0790-1

[8] Wang C, He J, Zhao T-H, Cao Y, Wang G, Sun B, Yan X, Guo W and Li M-H (2019) The Smaller the Leaf Is, the Faster the Leaf Water Loses in a Temperate Forest. Front. Plant Sci. 10:58. https://doi.org/10.3389/fpls.2019.00058

[9] Campillo C., García M., Daza C., Prieto M. Study of a Non-destructive Method for Estimating the Leaf Area Index in Vegetable Crops Using Digital Images, HortScience, 45:10. https://journals.ashs.org/hortsci/view/journals/hortsci/45/10/article-p1459.xml?rskey=0ngapT

 

[acespicoli]

Summary​

• The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques.
• Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves.
• We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number.
• Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities​

Manica Balant, Teresa Garnatje, Daniel Vitales, Oriane Hidalgo, Daniel H. Chitwood
First published: 17 May 2024

https://doi.org/10.1111/nph.19817


This is the dream, hopefully realized thru enough input...

and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

 

[acespicoli]

Plant Cell. 2010 Apr 30;22(4):1019–1032. doi: 10.1105/tpc.109.073601
Morphogenesis of Simple and Compound Leaves: A Critical Review

Abstract
The leaves of seed plants evolved from a primitive shoot system and are generated as determinate dorsiventral appendages at the flanks of radial indeterminate shoots. The remarkable variation of leaves has remained a constant source of fascination, and their developmental versatility has provided an advantageous platform to study genetic regulation of subtle, and sometimes transient, morphological changes. Here, we describe how eudicot plants recruited conserved shoot meristematic factors to regulate growth of the basic simple leaf blade and how subsets of these factors are subsequently re-employed to promote and maintain further organogenic potential. By comparing tractable genetic programs of species with different leaf types and evaluating the pros and cons of phylogenetic experimental procedures, we suggest that simple and compound leaves, and, by the same token, leaflets and serrations, are regulated by distinct ontogenetic programs. Finally, florigen, in its capacity as a general growth regulator, is presented as a new upper-tier systemic modulator in the patterning of compound leaves.

Ontogeny - Wikipedia

en.wikipedia.org

Leaf - Wikipedia

en.wikipedia.org

 

[acespicoli]

ECSD (most were fairly normal serrated)

DNL
»»» (RFK Skunk x Hawaiian) x Northern Lights

NL2 FROM:TODD AGCO PICS

 

[acespicoli]

TRIANGLE

 

[Bro.]

hi,

Some Non-serrated :

+++

 

[joepotsmoker]

This is a very interesting post I'll be following and posting a few things in if things go right, I Wana learn about this I grabbed a mutant strain (frostys purple freak) and I can't wait to see this thing grow...
But for bow here's my lettuce of a black kush I popped, lol the black kush is kn the bottom, the tiny gal is the freak

 

[joepotsmoker]

U got mad info here my dude, props

 

[joepotsmoker]

Here look at the leaf mutation on my Cinderella 99 I've never seen nothing like it

 

[acespicoli]

Here look at the leaf mutation on my Cinderella 99 I've never seen nothing like it

Had some thing like this will have see if I can find the pictures similar mutation seen once before, not sure of the origin
Very unique and interesting glad you shared this

Thanks

 

[joepotsmoker]

Had some thing like this will have see if I can find the pictures similar mutation seen once before, not sure of the origin
Very unique and interesting glad you shared this

Thanks

Deff, I'm getting into the mutated leaf market lol I may have some ducks footed stuff goin on later this year, well have to see what pulled through the genetics,
And yea, of course, this is how we learn, it's gona be a cool leaf I think, ill post when it's bigger, it just got transplanted.