A weakly supervised framework for high-resolution crop yield forecasts
Abstract
Predictor inputs and label data for crop yield forecasting are not always available at the same spatial resolution. We propose a deep learning framework that uses high resolution inputs and low resolution labels to produce crop yield forecasts for both spatial levels. The forecasting model is calibrated by weak supervision from low resolution crop area and yield statistics. We evaluated the framework by disaggregating regional yields in Europe from parent statistical regions to sub-regions for five countries (Germany, Spain, France, Hungary, Italy) and two crops (soft wheat and potatoes). Performance of weakly supervised models was compared with linear trend models and Gradient-Boosted Decision Trees (GBDT). Higher resolution crop yield forecasts are useful to policymakers and other stakeholders. Weakly supervised deep learning methods provide a way to produce such forecasts even in the absence of high resolution yield data.
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Published as:
D.R. Paudel,
D. Marcos,
A. de Wit,
H. Boogaard,
I. N. Athanasiadis,
A weakly supervised framework for high-resolution crop yield forecasts,
Computing Research Repository,
AI for Earth Sciences Workshop at 10th Int'l Conf Learning Representations (ICLR 2022),
2022, doi:10.48550/arXiv.2205.09016.
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