Modelling with Leaf Reflectance Spectroscopy data

Quickstart

1. Create an environment with Python 3.9, and install requirements (pip -r requirements.txt)
2. Execute python start.py and follow prompts in the terminal.
3. At the end of each program execution, the displayed results are saved to a folder called results. The PNG contains the confusion matrix, and the CSV file with the same name contains the performance summary (accuracy et al.).

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Using a different CSV of data

For example, if you'd like to manually filter the rows in a way that's not offered within the program itself.

Requirements:

• The CSV file must have the same column headers -- no changes to the names or order, and no adding/removing columns.
• It should use the same naming convention in the species column, i.e. 2 charaters for genus, 2 characters for subgenus, 2 characters for subsection, a period, and the species name.

To load your own custom CSV, specify the file path as an argument, e.g.:

python start.py data/my_new_file.csv.

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Credits

Field Museum of Natural History & Grainger Bioinformatics Center

Ryan Fuller (Postdoctoral Researcher), Beth McDonald (Machine Learning Engineer), Dr. Rick Ree (Curator & Section Head of Flowering Plants)

Please contact Ryan Fuller for research inquiries and licensing.