WSUAG-Arduino App

Project summary

One-sentence description of the project

Machine-Learning based image processing of agricultural data.

Additional information about the project

The WSUAG-Arduino App project is a custom solution designed to process images and automatically extract crop digital data from the Washington State University AGIcam system. This pipeline utilizes TensorFlow to detect a reference file and apply radiometric correction to the images. A TensorFlow model is also applied to segment the crops by plot in order to ascertain their statuses over time.

Installation

Prerequisites

• python 3.9 or greater
• python venv

Add-ons

See requirements.txt for list of packages used in the environment

Installation Steps

Steps: \ Clone repo - git clone https://github.com/WSUCptSCapstone-F23-S24/wsuag-arduinoapp.git \ Either use the installation script by running python .\installation\setup.py \ If that fails, in the terminal do the following: \ Create a python virtual environment by running python -m venv yolo8_venv \ Activate the environment by running .\yolo8_venv\Scripts\activate \ Run python -m pip install -r installation\requirements.txt to install all the relevant packages \ Finally, you are able to run the program, with python predict.py

Functionality

For functionality specifically running our model on iamges, first open repo in a conda environment. \ Change the directory to the object_detection folder. \ Add images to the crop_images folder that you would like to run the model on. \ run the command: python .\detect_from_image.py -m ._inference_graph\saved_model\ -l .\labelmap.pbtxt -i .\test_images\crop_test \ check the folder called ouput for the annotated pictures.

Known Problems

A known issue in our project is cloning this repository. This is because our git repo uses "Large File Storage" (LFS) to store files larger than 100 mb which we do have. \ When pulling the repo some files are not acccessbile so we have to run these commands in the terminal:

• git lfs fetch
• git lfs checkout

Contributing

1. Fork it!
2. Create your feature branch: git checkout -b my-new-feature
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin my-new-feature
5. Submit a pull request :D

Additional Documentation

• Sprint 1 Report
• Sprint 2 Report
• Sprint 3 Report
• Sprint 4 Report
• Sprint 5 Report
• Sprint 7 Report
• Video Demos
• Project Description
• Solution Approach
• Test Plans
• Conda Libraries

License

MIT License

Copyright (c) 2023 wsuag-arduinoapp

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.