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deep-transfer-learning-crop-prediction

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Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018. Best Presentation Winner.

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# Deep  Transfer  Learning  for  Crop  Yield  Prediction  with  Remote Sensing  Data

This project implements the deep learning architectures from [You et al. 2017](http://sustain.stanford.edu/crop-yield-analysis/) and applies them to developing countries with significant agricultural productivity (Argentina, Brazil, India).

We also examine the efficacy of transfer learning of yield forecasting insights between adjoining countries; some results were published in the proceedings of [COMPASS 2018](https://acmcompass.org/program/). Our paper can be viewed [here](https://www.dropbox.com/s/ei49eck573yxi6f/deep-transfer-learning.pdf?dl=0).

Contributers:
[Anna X Wang](annaxw@cs.stanford.edu), [Caelin Tran](caelin@cs.stanford.edu), [Nikhil Desai](nikhild@cs.stanford.edu), Professor David Lobell, Professor Stefano Ermon

# Requirements
- A Google Earth Engine account (for imagery retrieval)
- A Google Cloud storage account (for image data storage and access)
- A Google Cloud compute instance with python2 and GDAL

# Instructions
For any of these scripts, `python <script>.py -h` will provide a CLI usage string with explanations of each parameter.

## Instructions for creating a dataset
Steps marked with (`#`) should be done if the country of interest is not the US, India, Brazil, Argentina, or Ethiopia. See commit [9f7f43](https://github.com/AnnaXWang/deep-transfer-learning-crop-prediction/commit/9f7f4376972e9cefebbb19e7844301332c33d138) in this repository for an example of adding a new country (Ethiopia).

1. (`#`) Create a Google Earth Engine table from a shapefile of your new country's level2 boundaries.
1. (`#`) Add your country to `pull_modis.py` configuration - you will need the identifier of the shapefile table in GEE, and also need to add instructions on how to extract relevant metadata (e.g. a human-readable name) from a feature in the shapefile. More detail are in the comments in `pull_modis.py`.
1. Run `pull_modis.py` with country and imagery type to download imagery to a Google Cloud bucket
2. Put satellite imagery into "sat" folder, temperature images into "temp" folder, cover images into "cover" folder
3. Run `histograms.py` with the sat,temp,cover folders specified arguments - outputs to a "histograms" folder
4. (`#`) Save a CSV containing yields for relevant set of regions, harvest years, and crop types. (TODO: more explanation of the yields CSV format)
4. Run `make_datasets.py` with the "histograms" folder and yields CSV, along with relevant parameters for use in ML (train/test split, years to ignore or to use, etc) - creates a "dataset" folder containing numpy arrays which will be used by training/testing architecture

## Instructions for training the model
1. Run `train_NN.py` with a dataset name and neural net architecture type (CNN or LSTM). Generates a "log" folder containing the model weights, model predictions, and logs tracking model error.
2. Look inside the log folder for model results.

## Instructions for fine-tuning a trained model on a different dataset
1. Run `train_NN.py` on a dataset folder "X" as above. Generates a "log" folder.
1. Run `test_NN.py` and pass in as arguments the "log" folder from training, along with a new dataset folder "Y" on which to fine-tune the model.
2. Result is a new folder "log2" containing the new model weights, predictions, and error logs for performance on dataset "Y".

deep-transfer-learning-crop-prediction

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