Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach

• Published in: PloS one Vol. 15; no. 5; p. e0233382
• Main Authors: Khaki, Saeed, Khalilzadeh, Zahra, Wang, Lizhi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.05.2020; Public Library of Science (PLoS)
• Subjects: Agricultural production; Artificial neural networks; Biology and Life Sciences; Budgets; Collaboration; Computer and Information Sciences; Computer applications; Corn; Crop yields; Crosses, Genetic; Design of experiments; Factorization; Filtration; Forecasts and trends; Genomes; Hybridization, Genetic; Hybrids; Inbreeding; Machine learning; Methods; Models, Genetic; Neural networks; Neural Networks, Computer; Performance prediction; Plant Breeding; Research and Analysis Methods; Support vector machines; Technology application; Time; Yield; Zea mays - genetics; United States; United States > US; Iowa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0233382

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 a neural network to solve this problem. Our computational results suggested that the proposed model significantly outperformed other models such as deep factorization machines (DeepFM), generalized matrix factorization (GMF), LASSO, random forest (RF), and neural networks. Presented method and results were produced within the 2020 Syngenta Crop Challenge.