您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Use Self-Training Random Forest for Predicting Winter Wheat Yield

文献类型： 外文期刊

作者： Shen, Yulin 1 ; Mercatoris, Benoit 2 ; Liu, Qingzhi 3 ; Yao, Hongxun 4 ; Li, Zongpeng 5 ; Chen, Zhen 5 ; Wang, Wensheng 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

2.Univ Liege, TERRA, Biosyst Dynam & Exchanges, Gembloux Agrobio Tech, B-5030 Gembloux, Belgium

3.Wageningen Univ & Res, Informat Technol Grp, NL-6704 Wageningen, Netherlands

4.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China

5.Chinese Acad Agr Sci, Farmland Irrigat Res Inst, Henan Key Lab Water Saving Agr, Xinxiang 453002, Peoples R China

关键词： multispectral; wheat yield; self-training random forest

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 24 期

页码：

收录情况： SCI

摘要： The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wheat varieties during the 2019-2020 and 2021-2022 growing seasons. The testing results indicated that our model achieved an R-2 of 0.84, RMSE of 627.94 kg/ha, and MAE of 516.94 kg/ha in the test dataset, while the validation dataset yielded an R-2 of 0.81, RMSE of 692.96 kg/ha, and MAE of 550.62 kg/ha. In comparison, the standard random forest resulted in an R-2 of 0.81, RMSE of 681.02 kg/ha, and MAE of 568.97 kg/ha in the test dataset, with validation results of an R-2 of 0.79, RMSE of 736.24 kg/ha, and MAE of 585.85 kg/ha. Overall, these results demonstrate that our self-training random forest algorithm is a practical and effective solution for expanding annotated datasets, thereby enhancing the prediction accuracy of ML models in winter wheat yield forecasting.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台