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Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States
GIScience & Remote Sensing ( IF 6.0 ) Pub Date : 2024-05-09 , DOI: 10.1080/15481603.2024.2349341 Yuanchao Li 1, 2, 3 , Hongwei Zeng 1, 2 , Miao Zhang 1 , Bingfang Wu 1, 2 , Xingli Qin 1
GIScience & Remote Sensing ( IF 6.0 ) Pub Date : 2024-05-09 , DOI: 10.1080/15481603.2024.2349341 Yuanchao Li 1, 2, 3 , Hongwei Zeng 1, 2 , Miao Zhang 1 , Bingfang Wu 1, 2 , Xingli Qin 1
Affiliation
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 differ...
中文翻译:
全球去趋势显着提高了基于 XGBoost 的美国中西部县级玉米和大豆产量预测的准确性
机器学习在作物产量预测中的应用已获得相当大的关注,但产量趋势对这些预测的影响以及不同的结果仍然存在不确定性。
更新日期:2024-05-10
中文翻译:
全球去趋势显着提高了基于 XGBoost 的美国中西部县级玉米和大豆产量预测的准确性
机器学习在作物产量预测中的应用已获得相当大的关注,但产量趋势对这些预测的影响以及不同的结果仍然存在不确定性。