Crop Yield Prediction -- Machine Learning over Satellite Images
Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
Crop yield prediction is central in ensuring the food security. We introduce a scalable, accurate, and inexpensive method to predict crop yield using publicly available remote sensing data. A novel dimensionality reduction method is proposed based on histogram calculation. By combining Gaussian Process with Convolutional Neural Networks or Long-short Term Memory, the model significantly outperforms traditional models, and we show that the features automatically extracted by deep learning models are much more effective than traditional hand-crafted features.