您好，欢迎访问中国热带农业科学院 机构知识库！
中国热带农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Inversion of maize leaf nitrogen using UAV hyperspectral imagery in breeding fields

文献类型： 外文期刊

作者： Cheng, Qiwen 1 ; Wu, Bingsun 2 ; Ye, Huichun 3 ; Liang, Yongyi 1 ; Che, Yingpu 5 ; Guo, Anting 3 ; Wang, Zixuan 1 ; Tao, Zhiqiang 1 ; Li, Wenwei 1 ; Wang, Jingjing 1 ;

作者机构： 1.Hainan Univ, Sch Trop Agr & Forestry, Haikou 570228, Peoples R China

2.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Peoples R China

3.Hainan Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Hainan, Peoples R China

4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China

5.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China

关键词： maize; nitrogen; hyperspectral imagery; vegetation index; UAV; random forest regression; support vector regression

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.5 ）

ISSN： 1934-6344

年卷期： 2024 年 17 卷 3 期

页码：

收录情况： SCI

摘要： Nitrogen (N) as a pivotal factor in influencing the growth, development, and yield of maize. Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture, based on unmanned aerial vehicle (UAV) remote sensing technology. In this study, the hyperspectral images were acquired by UAV and the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) were measured to estimate the N nutrition status of maize. 24 vegetation indices (VIs) were constructed using hyperspectral images, and four prediction models were used to estimate the LNC and LNA of maize. The models include a single linear regression model, multivariable linear regression (MLR) model, random forest regression (RFR) model, and support vector regression (SVR) model. Moreover, the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields. The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll (NDchl) had the highest prediction accuracy for LNC (R2, R 2 , RMSE, and RE were 0.72, 0.21, and 12.19%, respectively) and LNA (R2, R 2 , RMSE, and RE were 0.77, 0.26, and 14.34%, respectively). And then, 24 VIs were divided into 13 important VIs and 11 unimportant VIs. Three prediction models for LNC and LNA were constructed using 13 important VIs, and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model, in which RFR model had the highest prediction accuracy for the validation dataset of LNC (R2, R 2 , RMSE, and RE were 0.78, 0.16, and 8.83%, respectively) and LNA (R2, R 2 , RMSE, and RE were 0.85, 0.19, and 9.88%, respectively). This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.

  • 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：中国热带农业科学院

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