Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States
The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differences between the detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to...
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| Главные авторы: | , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Taylor & Francis Group
2024-12-01
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| Серии: | GIScience & Remote Sensing |
| Предметы: | |
| Online-ссылка: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2349341 |
| Метки: |
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