A Hybrid Deep Learning-based Approach for Optimal Genotype by
  Environment Selection
Zahra Khalilzadeh Motahareh Kashanian Saeed Khaki Lizhi Wang
Precise crop yield prediction is essential for improving agricultural
practices and ensuring crop resilience in varying climates. Integrating weather
data across the growing season, especially for different crop varieties, is
crucial for understanding their adaptability in the face of climate change. In
the MLCAS2021 Crop Yield Prediction Challenge, we utilized a dataset comprising
93,028 training records to forecast yields for 10,337 test records, covering
159 locations across 28 U.S. states and Canadian provinces over 13 years
(2003-2015). This dataset included details on 5,838 distinct genotypes and
daily weather data for a 214-day growing season, enabling comprehensive
analysis. As one of the winning teams, we developed two novel convolutional
neural network (CNN) architectures: the CNN-DNN model, combining CNN and
fully-connected networks, and the CNN-LSTM-DNN model, with an added LSTM layer
for weather variables. Leveraging the Generalized Ensemble Method (GEM), we
determined optimal model weights, resulting in superior performance compared to
baseline models. The GEM model achieved lower RMSE (5.55% to 39.88%), reduced
MAE (5.34% to 43.76%), and higher correlation coefficients (1.1% to 10.79%)
when evaluated on test data. We applied the CNN-DNN model to identify
top-performing genotypes for various locations and weather conditions, aiding
genotype selection based on weather variables. Our data-driven approach is
valuable for scenarios with limited testing years. Additionally, a feature
importance analysis using RMSE change highlighted the significance of location,
MG, year, and genotype, along with the importance of weather variables MDNI and
AP.