Yield performance estimation of corn hybrids using machine learning algorithms
| Autor: | Michele Porta, Panos M. Pardalos, Farnaz Babaie Sarijaloo, Bijan Taslimi |
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| Rok vydání: | 2021 |
| Předmět: |
Boosting (machine learning)
Mean squared error Yield (finance) Data analysis Decision tree Machine learning computer.software_genre Agricultural data Artificial Intelligence Computer Science (miscellaneous) Engineering (miscellaneous) Mathematics Hybrid Artificial neural network business.industry Agriculture Yield prediction Computer Science Applications Random forest Corn hybrids Gradient boosting Artificial intelligence General Agricultural and Biological Sciences business computer Algorithm |
| Zdroj: | Artificial Intelligence in Agriculture, Vol 5, Iss, Pp 82-89 (2021) |
| ISSN: | 2589-7217 |
| DOI: | 10.1016/j.aiia.2021.05.001 |
| Popis: | Estimation of yield performance for crop products is a topic of interest in agriculture. In breeding programs, we cannot test all possible hybrids created by crossing two parents (inbred and tester) since it would be too time consuming and costly. In this paper, we exploit different machine learning algorithms including decision tree, gradient boosting machine, random forest, adaptive boosting, XGBoost and neural network to predict the yield of corn hybrids using data provided in the 2020 Syngenta Crop Challenge. The participants were asked to predict the yield of missing hybrids which were not tested before. Our results show that the prediction obtained by XGBoost is more accurate than other models with a root mean square error equal to 0.0524. Therefore, we use XGBoost model to estimate the yield performance for untested combinations of inbreds and testers. Using this approach, we identify hybrids with high predicted yield that can be bred to increase corn production. |
| Databáze: | OpenAIRE |
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