Traditionally, walnut kernel color is scored by a human on a four-point
color scale ranging from extra light to Amber, developed by the
California DFA. Two important reasons for using a high throughput
machine vision system for kernel phenotyping is to obtain better
quantitative resolution to use in molecular breeding and to avoid errors
in human vision phenotyping. RGB and LAB values give much more depth to
qualitative calculations, and machine data is far more consistent than
human scoring. Previous studies on walnut kernel imaging utilized a
thresholding-based approach to segment walnut kernels from their
background, however, detection accuracy can be improved. In this study,
we make changes to detection methods, primarily using PyTorch computer
vision processing and an improved thresholding method to better segment
walnut kernels. The PyTorch CNN pipeline allows us to use specific
photos to train a model, and the model can segment photos without the
need to figure out image thresholds. Our proposed thresholding method
uses the magick package in R instead of the ImageJ macros that were
previously used. After taking photos with a CVS, we used the CNN model
as well as an R script to identify and segment kernels from the
background. Our preliminary data shows that with enough training, the
CNN model is more robust in edge cases where we have overlapping kernels
or shifted images. This paper will focus on exploring the differences
between the three thresholding methods, and use the best method for
future breeding projects.