Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data
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