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Plant image analysis

> The pliman (plant image analysis) package is designed to analyze plant images, particularly for leaf and seed analysis. It offers a range of functionalities to assist with various tasks such as measuring disease severity, counting lesions, obtaining lesion shapes, counting objects in an image, extracting object characteristics, performing Fourier Analysis, obtaining RGB values, extracting object coordinates and outlines, isolating objects, and plotting object measurements.

Pedigree pruning

ggroups: an R package for pedigree and genetic groups data

> Most documents go through several versions (always more than you expected) before they are finally finished. Accordingly, you should do whatever possible to make the job of changing them easy.

> First, when you do the purely mechanical operations of typing, type so subsequent editing will be easy. Start each sentence on a new line. Make lines short, and break lines at natural places, such as after commas and semicolons, rather than randomly. Since most people change documents by rewriting phrases and adding, deleting and rearranging sentences, these precautions simplify any editing you have to do later. —Brian W. Kernighan (1974)

Genetic competition

Good explanation on fitting competition models in forest genetic trials using asreml:

Competitive Genetics: Exploring the impact of direct and indirect genetic effects in tree breeding.

Based on the work by Joao Costa e Silva and Richard Kerr “Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials”

Physics of coffee

I’m often fascinated by the lengths people will go to get something they like, as in “Reaching Fuller Flavor Profiles with the AeroPress”

Getting rid of convergence information in asreml

I am coding simulations where I call asreml() and it was taking longer than necessary, because it was sending the convergence information to the screen. The trick to get rid of that is to use the trace = FALSE option as in

asreml(trait_1 ~ 1, random = ~ mum, data = current_trial, trace = FALSE)

A good entry point to Julia

Danielle Navarro wrote a three-post series introducing Julia almost from scratch. Fantastic work. Bookmarking.

* A foundation in Julia
* Working with data in Julia
* Plotting data in Julia

Improving genomic selection

Would data augmentation improve the performance of within-family selection in forestry datasets?

Data Augmentation Enhances Plant-Genomic-Enabled Predictions

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