Assessment of the herbicide efficiency on treating invasive species: the case of reed canary grass using machine learning.

Note: go over to the qgis-app branch to look at the source code for the QGIS app & GUI. DO NOT MERGE that branch with maximum-likelihood or random-forest branch. The 3 different branches in this repo have different functionality and usecases.

Authors: Abhi Balijepalli, Logan Kleditz, Nuocheng Shen

The images/ dir is empty because each image file is over 1.5GB. Images are available on the shared T-Drive

Requirements before you get started:

• A windows computer (Mac can work, but we had more issues on there. No M1 Macs please)
• Access to the forestry department T: drive
• Download Stable QGIS (https://qgis.org/en/site/forusers/download.html)
• Make sure you have python 3.95 and have it's path built into your Windows computer (Mac has it by default)
• Read through the read-mle-rois.py file linked below to understand the actual algorithm
• Navigate to the qgis-app branch and understand the README.md there
• Experiment on random-forest branch after you have the QGIS plugin imported into QGIS and there are no path issues with python
• Read the links below!

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Links:

• Mason's Docs:
  • https://docs.google.com/document/d/1k4T7pBsiyCaiF9smgR2OKtg_lHBtuwnwEB0EHXQpxI8/edit
• Abhi & Mason's Source Code:
  • https://github.com/OSU-MARS/QGIS/blob/main/read-mle-rois.py