  Experimental corn hybrids are created in plant breeding programs by crossing
two parents, so-called inbred and tester, together. Identification of best
parent combinations for crossing is challenging since the total number of
possible cross combinations of parents is large and it is impractical to test
all possible cross combinations due to limited resources of time and budget. In
the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that
recorded the historical yield performances of around 4% of total cross
combinations of 593 inbreds with 496 testers which were planted in 280
locations between 2016 and 2018 and asked participants to predict the yield
performance of cross combinations of inbreds and testers that have not been
planted based on the historical yield data collected from crossing other
inbreds and testers. In this paper, we present a collaborative filtering method
which is an ensemble of matrix factorization method and neural networks to
solve this problem. Our computational results suggested that the proposed model
significantly outperformed other models such as LASSO, random forest (RF), and
neural networks. Presented method and results were produced within the 2020
Syngenta Crop Challenge.
