# A weakly supervised framework for high-resolution crop yield forecasts

arXiv: https://arxiv.org/abs/2205.09016 · HTML: https://mlsanity.com/paper/2205.09016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A CNN-RNN Framework for Crop Yield Prediction](https://mlsanity.com/paper/1911.09045/) (2019)
- [A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction](https://mlsanity.com/paper/2111.08900/) (2021)
- [Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt](https://mlsanity.com/paper/2008.04060/) (2020)
- [Corn Yield Prediction with Ensemble CNN-DNN](https://mlsanity.com/paper/2105.14351/) (2021)
- [A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery](https://mlsanity.com/paper/1810.09528/) (2018)

## Built on by (most cited first, 2 of 2)

- [From General to Specialized: The Need for Foundational Models in Agriculture](https://mlsanity.com/paper/2507.05390/) (2025)
- [From Rows to Yields: How Foundation Models for Tabular Data Simplify Crop Yield Prediction](https://mlsanity.com/paper/2506.19046/) (2025)

## Abstract (arXiv)

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.