Seminar, Introduction to Digital Agro. We will consider how we can use crop simulation models and optimization methods to find optimal strategies.
Optimization and machine learning - pdf
Open Seminar_Optimization.ipynb in Google Colab!
Open Plots_for_optimizers.ipynb in Google Colab!
Crop yield prediction based on Machine Learning
PCSE/WOFOST - Python Crop Simulator Environment
https://pcse.readthedocs.io/en/stable/
Nevergrad - A gradient-free optimization platform
https://salib.readthedocs.io/en/latest/
Clone this repository and create new conda env on your local machine
git clone https://github.com/EDSEL-skoltech/Intro_to_Digital_Agriculture
Create new env with pcse package for crop models WOFOST
cd Agro_Optimization
conda env create -f py3_pcse.yml
conda activate py3_pcse
Mikhail Gasanov – [email protected]
Distributed under the MIT license. See LICENSE for more information.
- Fork it (https://github.com/EDSEL-skoltech/Intro_to_Digital_Agriculturefork)
- Create your feature branch (
git checkout -b feature/fooBar) - Commit your changes (
git commit -am 'Add some fooBar') - Push to the branch (
git push origin feature/fooBar) - Create a new Pull Request