CropGRM

CropGRM is a general model for crop spatial recognition based on time series of remote sensing and climate data. It can predict 12 crop types globally.

Features

• Recognizes 12 crops: winter wheat, spring wheat, spring oats, spring barley, spring rye, spring canola, sunflower, corn, soybean, sorghum, beet, potato.
• Uses spectral (Landsat 5, 8, 9), phenological, and climate (ERA5) features.
• Trained on global crop data from Europe, the USA, Canada, and China.
• Gradient boosting model implemented with Catboost.

Project structure

CropGRM
├── data # test data
│   ├── final # output files after prediction
│   ├── processed # preprocessed data for model predictions
│   └── raw # input files
├── models # pre-trained models
├── notebooks # examples and instructions
├── README.md
└── requirements.txt

Models

The project includes three base models with different numbers of predictors and a fine-tuned model (available in models):

• CropGRM-large.cbm - 134 features
• CropGRM-optimized.cbm - 82 features
• CropGRM-small.cbm - 24 features
• finetuned_model.cbm - fine-tuned CropGRM-small on local scientific center data

Feature lists are in notebooks/inference_tabular.ipynb.

Examples

Examples and instructions are available in notebooks:

• gee_downloading.ipynb - instructions and examples for downloading spectral and meteorological data from Google Earth Engine Platform
• feature_calculation.ipynb - instructions and examples for calculating spectral, phenological and meteorological feature for model input
• inference_tabular.ipynb - instructions and examples for making tabular predictions including feature lists for different models
• inference_geospatial.ipynb - instructions and examples for making geospatial predictions

Tested on Python 3.12.

Input data

• Vector dataset with field geometries (example available at data/raw/fields.fgb)
• Meteorological data (ERA5) downloaded from Google Earth Engine (example available at data/raw/fields_meteo_data.csv)
• Spectral data (Landsat 5, 8, 9) downloaded from Google Earth Engine (example available at data/raw/fields_spectral_data.csv)
• Preprocessed feature dataset from Google Earth Engine for each field (example available at data/processed/input_data_for_model.parquet)

The two input datasets should have the same unique identifier for joining data.

The results can be obtained in tabular, vector, or raster format.

License and Attribution

All trained models, datasets, and example notebooks in this repository are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (see LICENSE).

Any use of the models - academic, commercial, or derivative - must include proper attribution. Please cite this repository when using, modifying, or distributing the models or derivative works.

Example attribution:

This work uses models published in the repository https://github.com/AgroDT/CropGRM, licensed under CC BY 4.0.