摘要
【目的】利用无人机多光谱技术监测棉花冠层叶片的叶绿素含量。【方法】通过无人机获取新疆南疆地区棉花冠层的多光谱图像,选取7种植被指数,利用7种不同的反演方法估算棉花关键生育时期花铃期的叶绿素含量,包括基于线性回归(linear regression,LR)的一元线性回归、偏最小二乘回归(partial least squares regression,PLSR)、岭回归(ridge regression,RR)、最小绝对值收敛和选择算子(least absolute shrinkage and selection operator,LASSO)回归,以及支持向量回归(support vector regression,SVR)、K近邻回归(K nearest neighbors regression,KNNR)、随机森林回归(random forest regression,RFR)。【结果】与线性回归模型相比,RFR、SVR和KNNR算法提高了棉花冠层叶绿素含量估算模型精度,尤其是RFR算法,其模型验证集的决定系数为0.742,均方根误差为1.158 mg·L^(-1),相对分析误差为1.969。【结论】利用RFR机器学习方法构建的基于无人机多光谱影像的棉花冠层叶片叶绿素含量估算模型可及时、准确地判断棉花的生长状况,为棉田精准管理提供技术支撑。
[Objective]This study aims to monitor the chlorophyll content of cotton leaves by utilizing unmanned aerial vehicle(UAV)-based multispectral technology.[Methods]This study utilized multispectral images of cotton canopies obtained by UAV in southern Xinjiang and employed seven different machine learning methods to estimate the canopy chlorophyll content during the flowering and boll-setting stage which is the critical growth period of cotton.The seven methods include linear regression(LR)-based methods,i.e.,simple linear regression,partial least squares regression(PLSR),ridge regression(RR),least absolute shrinkage and selection operator(LASSO)regression,support vector regression(SVR),K-nearest neighbors regression(KNNR),and random forest regression(RFR).[Results]The results showed that compared with the LR method,the RFR,SVR and KNNR can improve the accuracy of prediction model of chlorophyll content in cotton canopies,especially the RFR algorithm,which had the coefficient of determination of 0.742,root mean square error of 1.158 mg·L^(-1),residual predictive deviation of 1.969 with the validation dataset.[Conclusion]The use of UAV-based multispectral images with the RFR machine learning method,exhibits the most outstanding performance to estimate the chlorophyll content of cotton leaves and provide essential technical support for precision management in cotton field.
作者
赵鑫
李朝阳
王洪博
刘江凡
江文格
赵泽艺
王兴鹏
高阳
Zhao Xin;Li Zhaoyang;Wang Hongbo;Liu Jiangfan;Jiang Wenge;Zhao Zeyi;Wang Xingpeng;Gao Yang(College of Water Resources and Architecture Engineering,Tarim University,Aral,Xinjiang 843300,China;Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region,Tarim University,Aral,Xinjiang 843300,China;Key Laboratory of Tarim Oasis Agriculture,Ministry of Education,Tarim University,Aral,Xinjiang 843300,China;Key Laboratory of Northwest Oasis Water-Saving Agriculture,Ministry of Agriculture and Rural Affairs,Shihezi,Xinjiang 832000,China;Western Agricultural Research Center,Chinese Academy of Agricultural Sciences,Changji,Xinjiang 831100,China;Institute of Farmland Irrigation,Chinese Academy of Agricultural Sciences,Xinxiang,Henan 453002,China)
出处
《棉花学报》
CSCD
北大核心
2024年第1期1-13,共13页
Cotton Science
基金
新疆生产建设兵团财政科技计划项目(2022BC009)
现代农业工作重点实验室2022年度开放课题项目(TDNG2022103)
中央级科研院所基本科研业务费专项(中国农业科学院农田灌溉研究所)资助项目(IFI2023-19)
国家重点研发计划(2022YFD1900505)
塔里木大学校级研创项目(TDGRI202253)。
关键词
无人机
多光谱
叶绿素含量
机器学习
遥感反演
棉花
UAV
multispectral imagery
chlorophyll content
machine learning
remote sensing inversion
cotton
