A weakly supervised framework for high-resolution crop yield forecasts

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 calibr...

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Bibliographic Details
Published inarXiv.org
Main Authors Paudel, Dilli R, Marcos, Diego, Allard de Wit, Boogaard, Hendrik, Athanasiadis, Ioannis N
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.05.2022
Subjects
Agricultural production
Crop yield
Decision trees
Deep learning
Forecasting
High resolution
Mathematical models
Spatial resolution
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Summary: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.
ISSN:2331-8422