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Environment setup for Python 3.11

Create a new virtual environment with

python3 -m venv env

activate it

source env/bin/activate

install requirements

pip3 install -r requirements.txt

change directory

cd src/

install package

python setup.py install

Data

The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments. Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].

Paper self-supervised learning

The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.

Yields

The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.

Citation

These data sets include yields and crop types and were introduced in following publications:

@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References

[1] Breizhcrops, https://breizhcrops.org
[2] Radiant Earth Foundation, https://www.radiant.earth/
[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2
[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/
[5] Lightly, https://www.lightly.ai/
[6] EuroCrops, https://www.eurocrops.tum.de/index.html

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
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Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
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Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
Resources
Readme
MIT license
Activity
Custom properties
Stars
23 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
Repository files navigation
Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
Resources
Readme
MIT license
Activity
Custom properties
Stars
23 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
Repository files navigation
Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
Resources
Readme
MIT license
Activity
Custom properties
Stars
23 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
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Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
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Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
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Readme
MIT license
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Stars
23 stars
Watchers
4 watching
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5 forks
Report repository
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Used by
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content

This repository was archived by the owner on Feb 9, 2026. It is now read-only.

BranchesTags
Repository files navigation
Environment setup for Python 3.11
Create a new virtual environment with
python3 -m venv env

activate it
source env/bin/activate

install requirements
pip3 install -r requirements.txt 
change directory
cd src/

install package
python setup.py install

Data
The notebooks directory contains examples for yield and crop types data. The script 'install_cropdata.sh' in scripts/data/ installs several data sources for crop types from different climate regions for domain adaptation experiments.
Nevertheless, the dataset was not expanded due to emerging data sources. For future experiments with crop types, there is now very interesting benchmark data such as EuroCrops [6].
Paper self-supervised learning
The paper used crop type data (time series) from Sentinel-2 for Bavaria to train a model using data from 2016 and 2017 and apply it to 2018. Experiments were also run with 5 and 10 percent data from 2018. 2018 has deviating climate conditions compared to 2016 and 2017. Two example python files for reproducing the results can be found under src/experiments/paper.
Yields
The yield data at the field level were part of the publication 'Prediction of multi-year winter wheat yields...' and were documented and collected in detailed investigations [4]. The example 'notebooks/demo_yields_data.ipynb' shows how to load yield data. It includes data from a combine harvester as well as time series for weather and Sentinel-2 data. The notebook 'yield_pred_pixel.ipynb' predicts yields for each pixel. In addition to the combine harvester data, high-precision weighed yield data is also available for each field to investigate predictions at the field level or to compare with combine harvester yields. Mainly winter wheat was considered, but some winter barley yields are also available.
Citation
These data sets include yields and crop types and were introduced in following publications:
@article{Data1,
title = {Prediction of multi-year winter wheat yields at the field level with satellite and climatological data},
journal = {Computers and Electronics in Agriculture},
volume = {194},
pages = {106777},
year = {2022},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2022.106777},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922000941},
author = {Michael Marszalek and Marco Körner and Urs Schmidhalter},
}
@article{Data2,
author = {Marszalek, Michael and Saux, B. and Mathieu, P.-P and Nowakowski, Artur and Springer, Daniel},
year = {2022},
month = {05},
pages = {1327-1333},
title = {SELF-SUPERVISED LEARNING – A WAY TO MINIMIZE TIME AND EFFORT FOR PRECISION AGRICULTURE?},
volume = {XLIII-B3-2022},
journal = {ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences},
doi = {10.5194/isprs-archives-XLIII-B3-2022-1327-2022}
}

References
[1] Breizhcrops, https://breizhcrops.org

[2] Radiant Earth Foundation, https://www.radiant.earth/

[3] Remelgado, R., Zaitov, S., Kenjabaev, S. et al. A crop type dataset for consistent land cover classification in Central Asia. Sci Data 7, 250 (2020). https://doi.org/10.1038/s41597-020-00591-2

[4] Chair of Plant Nutrition, TUM, https://www.pe.wzw.tum.de/

[5] Lightly, https://www.lightly.ai/

[6] EuroCrops, https://www.eurocrops.tum.de/index.html
About
Precision agriculture in the embedding space
Resources
Readme
MIT license
Activity
Custom properties
Stars
23 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages