Pycrop Yield Prediction

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

Data SciencePythonMIT

Abstract

Pycrop Yield Prediction is an open-source Data Science project. A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction. Deep Gaussian Processes combine the expressivity of Deep Neural Networks with Gaussian Processes' ability to leverage spatial and temporal correlations between data points. It is built using Python, Machine Learning, Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Deep Gaussian Processes combine the expressivity of Deep Neural Networks with Gaussian Processes' ability to leverage spatial and temporal correlations between data points.

In this pipeline, a Deep Gaussian Process is used to predict soybean yields in US counties.

A PyTorch implementation of Jiaxuan You's 2017 Crop Yield Prediction Project.

2. Objective

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

This project demonstrates how Python, Machine Learning, Deep Learning can be applied to a real-world Data Science problem.

4. Technology Stack

PythonMachine LearningDeep Learning

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/gabrieltseng/pycrop-yield-prediction.git
cd pycrop-yield-prediction
conda env create -f environment.yml
conda activate crop_yield_prediction
earthengine authenticate
python -c "import ee; ee.Initialize()"

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by gabrieltseng and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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