soilspectraml is an open-source initiative aimed at developing machine learning models for predictive soil spectroscopy, with a focus on accurate clay content prediction from mid-infrared (mir) soil spectra.
find a quick overview of soil spectroscopy and the ss4gg hackathon here.
this image illustrates the size-based classification of soil minerals, highlighting the distinctive attributes of clay in terms of texture, water retention, and nutrient interaction.
develop a machine learning model to accurately predict soil clay content across diverse mir instruments, using root mean squared error (rmse) as the performance metric.
a. authenticate with kaggle api:
- see this article for instructions on how to authenticate with the kaggle api:
c. download dataset:
- run the following command to download the dataset:
cd data/raw kaggle competitions download -c ss4gg-hackathon-mir-soil-spectroscopy - this command downloads the dataset for the ss4gg-hackathon-mir-soil-spectroscopy competition to the current directory (i.e.,
data/raw/).
- clay content prediction: utilizes mir soil spectroscopy for precise clay content estimation.
- interoperable models: ensures model consistency and accuracy across diverse mir instruments.
- open soil spectral library ossl utilization: leverages the ossl for robust model training and evaluation.
- machine learning framework: incorporates a modular ml framework allowing easy model iteration and evaluation.
- prerequisites:
- installation:
- dataset:
- training models:
- model evaluation:
we welcome contributions, bug reports, and feature requests. please refer to the contributing.md file for guidelines.
this project is licensed under the mit license. see the license.md file for details.
- repository: https://github.com/patmejia/soil_spectra_ml
- issue tracker: https://github.com/patmejia/soil_spectra_ml/issues
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ss4gg hackathon: mir soil spectroscopy modeling by zecojls, kaggle, 2023. link
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cookiecutter data science for the project structure guidelines