  Crop yield prediction is one of the tasks of Precision Agriculture that can
be automated based on multi-source periodic observations of the fields. We
tackle the yield prediction problem using a Convolutional Neural Network (CNN)
trained on data that combines radar satellite imagery and on-ground
information. We present a CNN architecture called Hyper3DNetReg that takes in a
multi-channel input image and outputs a two-dimensional raster, where each
pixel represents the predicted yield value of the corresponding input pixel. We
utilize radar data acquired from the Sentinel-1 satellites, while the on-ground
data correspond to a set of six raster features: nitrogen rate applied,
precipitation, slope, elevation, topographic position index (TPI), and aspect.
We use data collected during the early stage of the winter wheat growing season
(March) to predict yield values during the harvest season (August). We present
experiments over four fields of winter wheat and show that our proposed
methodology yields better results than five compared methods, including
multiple linear regression, an ensemble of feedforward networks using AdaBoost,
a stacked autoencoder, and two other CNN architectures.
