  High-throughput phenotyping refers to the non-destructive and efficient
evaluation of plant phenotypes. In recent years, it has been coupled with
machine learning in order to improve the process of phenotyping plants by
increasing efficiency in handling large datasets and developing methods for the
extraction of specific traits. Previous studies have developed methods to
advance these challenges through the application of deep neural networks in
tandem with automated cameras; however, the datasets being studied often
excluded physical labels. In this study, we used a dataset provided by Oak
Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white
labels displaying treatment (control or drought), block, row, position, and
genotype. Optical character recognition (OCR) was used to read these labels on
the plants, image segmentation techniques in conjunction with machine learning
algorithms were used for morphological classifications, machine learning models
were used to predict treatment based on those classifications, and analyzed
encoded EXIF tags were used for the purpose of finding leaf size and
correlations between phenotypes. We found that our OCR model had an accuracy of
94.31% for non-null text extractions, allowing for the information to be
accurately placed in a spreadsheet. Our classification models identified leaf
shape, color, and level of brown splotches with an average accuracy of 62.82%,
and plant treatment with an accuracy of 60.08%. Finally, we identified a few
crucial pieces of information absent from the EXIF tags that prevented the
assessment of the leaf size. There was also missing information that prevented
the assessment of correlations between phenotypes and conditions. However,
future studies could improve upon this to allow for the assessment of these
features.
