Deep Transfer Learning Crop Prediction

Abstract

1. Introduction

This project implements the deep learning architectures from You et al. 2017 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. Our paper can be viewed here.

Contributers: Anna X Wang, Caelin Tran, Nikhil Desai, Professor David Lobell, Professor Stefano Ermon

2. Objective

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

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

4. Technology Stack

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/AnnaXWang/deep-transfer-learning-crop-prediction.git
cd deep-transfer-learning-crop-prediction

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 AnnaXWang 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.