Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

IF 6 2区地球科学 Q1 GEOGRAPHY, PHYSICAL

GIScience & Remote Sensing Pub Date : 2024-05-09 DOI:10.1080/15481603.2024.2349341

Yuanchao Li, Hongwei Zeng, Miao Zhang, Bingfang Wu, Xingli Qin

引用次数: 0

Abstract

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...

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全球去趋势显著提高了基于 XGBoost 的美国中西部县级玉米和大豆产量预测的准确性

机器学习在农作物产量预测中的应用已取得了相当大的进展，但产量趋势对这些预测的影响以及农作物产量预测的差异仍存在不确定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

GIScience & Remote Sensing 地学-遥感

CiteScore

11.20

自引率

9.00%

发文量

审稿时长

6 months

期刊介绍： GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.