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crop-forecasting

Predicting rice field yields through the integration of Microsoft Planetary satellite images, meteorological data, and field information in the 2023 EY Open Science Data Challenge - Crop Forecasting.

https://github.com/association-rosia/crop-forecasting

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.2%) to scientific vocabulary

Keywords

2023-ey-open-science-data-challenge agricultural-modelling agriculture-data crop-forecasting deep-learning meteorological-data microsoft-planetary-computer multimodal-deep-learning satellite-imagery
Last synced: 7 months ago · JSON representation ·

Repository

Predicting rice field yields through the integration of Microsoft Planetary satellite images, meteorological data, and field information in the 2023 EY Open Science Data Challenge - Crop Forecasting.

Basic Info
Statistics
  • Stars: 20
  • Watchers: 0
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Topics
2023-ey-open-science-data-challenge agricultural-modelling agriculture-data crop-forecasting deep-learning meteorological-data microsoft-planetary-computer multimodal-deep-learning satellite-imagery
Created almost 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

🍚 Crop Forecasting

The project 2023 EY Open Science Data Challenge - Crop Forecasting is a Data Science project conducted as part of the challenge proposed by EY, Microsoft, and Cornell University. The objective of this project is to predict the yield of rice fields using satellite image data provided by Microsoft Planetary, meteorological data, and field data.

🏆 Challenge ranking

The score of the challenge was the R2 score.
Our solution was the 4th (out of 185 teams) one with a R2 score equal to 0.66 🎉.

The podium:
🥇 Outatime - 0.68
🥈 Joshua Rexmond Nunoo Otoo - 0.68
🥉 Amma Simmons - 0.67

🛠️ Data processing

🏛️ Model architecture

📚 Documentation

The project documentation, generated using Sphinx, can be found in the docs/ directory. It provides detailed information about the project's setup, usage, implementation, tutorial.

🔬 References

Jeong, S., Ko, J., & Yeom, J. M. (2022). Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of The Total Environment, 802, 149726.

Nazir, A., Ullah, S., Saqib, Z. A., Abbas, A., Ali, A., Iqbal, M. S., ... & Butt, M. U. (2021). Estimation and forecasting of rice yield using phenology-based algorithm and linear regression model on sentinel-ii satellite data. Agriculture, 11(10), 1026.

📝 Citing

@misc{UrgellReberga:2023, Author = {Baptiste Urgell and Louis Reberga}, Title = {Crop forecasting}, Year = {2023}, Publisher = {GitHub}, Journal = {GitHub repository}, Howpublished = {\url{https://github.com/association-rosia/crop-forecasting}} }

🛡️ License

Project is distributed under MIT License

👨🏻‍💻 Contributors

Louis REBERGA

Baptiste URGELL

Owner

  • Name: RosIA
  • Login: association-rosia
  • Kind: organization
  • Location: France

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "URGELL"
  given-names: "Baptiste"
- family-names: "REBERGA"
  given-names: "Louis"
title: "GitHub repository"
publisher: "Github"
year: "2023"
version: 1.0
date-released: 2023-4-9
url: "https://github.com/association-rosia/crop-forecasting"
data: "Crop Yield Data - EY"

GitHub Events

Total
  • Watch event: 6
  • Fork event: 1
Last Year
  • Watch event: 6
  • Fork event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 266
  • Total Committers: 5
  • Avg Commits per committer: 53.2
  • Development Distribution Score (DDS): 0.477
Past Year
  • Commits: 266
  • Committers: 5
  • Avg Commits per committer: 53.2
  • Development Distribution Score (DDS): 0.477
Top Committers
Name Email Commits
BaptisteUrgell b****u@g****m 139
Louis REBERGA l****a@g****m 81
rbrgAlou 6****a 39
Baptiste Urgell 7****l 4
admin a****n@a****l 3

Issues and Pull Requests

Last synced: about 2 years ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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  • transiteration (1)
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Dependencies

environment.yml pypi
  • ipyleaflet *
  • odc-stac *
  • planetary-computer *
  • pystac *
  • pystac-client *
  • rioxarray *
  • stackstac *
  • wandb *
  • xarray-spatial *