{"title":"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","authors":"Yuanchao Li, Hongwei Zeng, Miao Zhang, Bingfang Wu, Xingli Qin","doi":"10.1080/15481603.2024.2349341","DOIUrl":null,"url":null,"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...","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States\",\"authors\":\"Yuanchao Li, Hongwei Zeng, Miao Zhang, Bingfang Wu, Xingli Qin\",\"doi\":\"10.1080/15481603.2024.2349341\",\"DOIUrl\":null,\"url\":null,\"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...\",\"PeriodicalId\":55091,\"journal\":{\"name\":\"GIScience & Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GIScience & Remote Sensing\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/15481603.2024.2349341\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GIScience & Remote Sensing","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/15481603.2024.2349341","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
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 differ...
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.
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