  Advanced machine learning techniques have been used in remote sensing (RS)
applications such as crop mapping and yield prediction, but remain
under-utilized for tracking crop progress. In this study, we demonstrate the
use of agronomic knowledge of crop growth drivers in a Long Short-Term
Memory-based, domain-guided neural network (DgNN) for in-season crop progress
estimation. The DgNN uses a branched structure and attention to separate
independent crop growth drivers and capture their varying importance throughout
the growing season. The DgNN is implemented for corn, using RS data in Iowa for
the period 2003-2019, with USDA crop progress reports used as ground truth.
State-wide DgNN performance shows significant improvement over sequential and
dense-only NN structures, and a widely-used Hidden Markov Model method. The
DgNN had a 4.0% higher Nash-Sutfliffe efficiency over all growth stages and 39%
more weeks with highest cosine similarity than the next best NN during test
years. The DgNN and Sequential NN were more robust during periods of abnormal
crop progress, though estimating the Silking-Grainfill transition was difficult
for all methods. Finally, Uniform Manifold Approximation and Projection
visualizations of layer activations showed how LSTM-based NNs separate crop
growth time-series differently from a dense-only structure. Results from this
study exhibit both the viability of NNs in crop growth stage estimation (CGSE)
and the benefits of using domain knowledge. The DgNN methodology presented here
can be extended to provide near-real time CGSE of other crops.
