https://github.com/esipfed/ag-net
Ag-Net: building a customized deep neural network for recognizing crop categories based on spectral characteristics.
https://github.com/esipfed/ag-net
Last synced: 10 months ago
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Ag-Net: building a customized deep neural network for recognizing crop categories based on spectral characteristics.
- Host: GitHub
- URL: https://github.com/esipfed/ag-net
- Owner: ESIPFed
- License: apache-2.0
- Created: 2019-05-10T19:23:53.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2020-09-12T14:14:22.000Z (almost 6 years ago)
- Last Synced: 2024-01-25T18:35:55.621Z (over 2 years ago)
- Language: Jupyter Notebook
- Size: 174 KB
- Stars: 5
- Watchers: 10
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Ag-Net
## Introduction
Ag-Net is a bundle of customized deep neural networks for recognizing crops based on spectral characteristics. The main idea is to customize the current neural networks like U-Net to make it more suitable for extracting and recognizing the season-changing features of various crop over the growing season. The ultimate goal is to generate high-quality accurate crop maps at any stage of crops rather than waiting for next year. The training and validation ground truth data comes from USGS EarthExplorer and USDA NASS.
## Training Datasets
We have created a free sample dataset for direct training and testing of neural network. [Ag-Net-Dataset](https://github.com/ZihengSun/Ag-Net-Dataset)
## Ag-Net Models
Ayush Patel, Jaypee University of Information Technology : [Agriculture Net.ipynb](https://github.com/1998at/AgricultureNet/blob/master/Agriculture%20Net.ipynb)
SANKALP MITTAL, BITS Pilani : [Ag-Net.ipynb](https://github.com/sankalpmittal1911-BitSian/AgriculturalNet-AgNet-/blob/master/agnet.ipynb) Training Accuracy: 91% Validation Accuracy: 84%
## Results
AgNet_NE_v1 dataset: Agricultural Map Archive of Nebraska, 1985 ~ 2000, Download [here](https://medium.com/@jensensunny/revisit-the-agricultural-history-of-nebraska-derived-by-earth-ai-35a7707347ff)
## Contributor
We have several talented students voluntarily working on this project as their summer projects.
If you would like to contribute to this project or list existing jupyterbooks or github repos in this page, please feel free to contact Dr. Ziheng Sun (zsun@gmu.edu) or Dr. Annie Burgess (annieburgess@esipfed.org).