it4lia/predictive_irrigation_models

Predictive Irrigation Models

This repository contains data preprocessing and analytics pipelines for the distribution of Predictive Irrigation Models, integrating data from field sensors, weather, soil, crop, and remote sensing sources.

Project Structure

predictive_irrigation_models/
├── config/
│ ├── params.yml
│ ├── xgcast_params.yml
│ ├── aquacrop_params.yml
│ └── fieldsensor_irrigator_mapping_anonym.yaml.yml
├── pipelines/
│ ├── __init__.py
│ ├── aquacrop_preparation_pipeline.py
│ ├── data_collection_pipeline.py
│ ├── aquacrop_preparation_pipeline.py
│ ├── demo_run.py
│ ├── model_preparation_pipeline.py
│ ├── preprocessing_pipeline.py
│ ├── resample_impute_pipeline.py
│ ├── soilcast_pipeline.py
│ ├── xgcast_pipeline.py
│ └── xgcast_run.py
├── data/
│ ├── 03_primary/
│ ├── 04_model_input/
│ ├── 05_aquacrop_input/
│ ├── 05_xgcast_input/
│ ├── 06_aquacrop_output/
│ └── 06_xgcast_output/
├── tools/
│ └── ...
└── README.md

Features

• Prefect-based Pipelines: Modular tasks and flows for data collection, transformation, and saving.
• Sensor & Weather Data Integration: Reads and merges raw sensor and weather data for multiple consortia.
• Soil, Crop, and Remote Sensing Data: Integrates geospatial and tabular data sources.
• Automated Testing: Prefect tasks and flows for validating preprocessing results.
• Artifact Logging: Data summary artifacts for monitoring pipeline outputs.

Getting Started

Prerequisites

• Python 3.8+
• Prefect
• uv

Install dependencies: uv sync

Configuration Edit config/params.yml to specify consortia names, data folders, and other parameters.

Running the Pipeline

From the project root, run:

python -m main

Running Tests

From the project root, run:

python -m test.test_results

Contributing

Feel free to open issues or submit pull requests for improvements or bug fixes.

License

MIT License

Contact

For questions or collaboration, please contact the repository owner. ``````