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.

本文提出一种利用高分辨率的指标和低分辨率的标记数据来预测作物产量的深度学习框架，并通过低分辨率作物面积和产量统计的弱监督进行校准。该框架的表现优于线性趋势模型和 GBDT，即使缺乏高分辨率产量数据，也可以用弱监督深度学习方法来预测更高分辨率的作物产量。

高分辨率作物产量预测的弱监督框架

A weakly supervised framework for high-resolution crop yield forecasts

Crop yield forecasting is the methodology of predicting crop yields prior to
harvest. The availability of accurate yield prediction frameworks have enormous
implications from multiple standpoints, including impact on the crop commodity
futures markets, formulation of agricultural policy, as well as crop insurance
rating. The focus of this work is to construct a corn yield predictor at the
county scale. Corn yield (forecasting) depends on a complex, interconnected set
of variables that include economic, agricultural, management and meteorological
factors. Conventional forecasting is either knowledge-based computer programs
(that simulate plant-weather-soil-management interactions) coupled with
targeted surveys or statistical model based. The former is limited by the need
for painstaking calibration, while the latter is limited to univariate analysis
or similar simplifying assumptions that fail to capture the complex
interdependencies affecting yield. In this paper, we propose a data-driven
approach that is "gray box" i.e. that seamlessly utilizes expert knowledge in
constructing a statistical network model for corn yield forecasting. Our
multivariate gray box model is developed on Bayesian network analysis to build
a Directed Acyclic Graph (DAG) between predictors and yield. Starting from a
complete graph connecting various carefully chosen variables and yield, expert
knowledge is used to prune or strengthen edges connecting variables.
Subsequently the structure (connectivity and edge weights) of the DAG that
maximizes the likelihood of observing the training data is identified via
optimization. We curated an extensive set of historical data (1948-2012) for
each of the 99 counties in Iowa as data to train the model.

本文提出了一种基于贝叶斯网络分析构建的、结合专家知识的统计网络模型的多变量灰盒玉米产量预测器，利用历史数据对 99 个县的数据进行训练，可用于精确预测作物产量。