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基于无人机影像技术的小麦长势遥感监测
引用本文:李强.基于无人机影像技术的小麦长势遥感监测[J].农机化研究,2022,44(5):193-197.
作者姓名:李强
作者单位:唐山工业职业技术学院, 河北 唐山 063299
基金项目:河北省重点研发计划项目(17275417);唐山市人才项目(A201903011);曹妃甸区科技计划项目(201808)。
摘    要:随着精准农业的发展,农作物长势监测越来越重要.传统的小麦长势监测主要依靠人工采样进行,作业效率低、监测范围小、耗费人力物力大.为有效提高小麦长势监测效率,引入无人机影像技术,以曹妃甸地区的小麦为研究对象,利用无人机影像技术和高光谱影像采集传感器完成对曹妃甸地区小麦叶面积指数、叶片生物量、叶绿素含量及叶片氮含量等长势参数...

关 键 词:无人机  遥感监测  长势参数  CGMI  PLSR

Remote Sensing Monitoring of Wheat Growth Based on UAV Image Technology
Li Qiang.Remote Sensing Monitoring of Wheat Growth Based on UAV Image Technology[J].Journal of Agricultural Mechanization Research,2022,44(5):193-197.
Authors:Li Qiang
Institution:(Tangshan Polytechnic College,Tangshan 063299,China)
Abstract:With the development of precision agriculture, the importance of crop growth monitoring is increasing. Traditional wheat growth monitoring mainly relies on manual sampling, which has low operating efficiency, small monitoring range, and consumes a lot of manpower and material resources. In order to effectively improve the monitoring efficiency of wheat growth, this paper introduces UAV influence technology, taking wheat in Caofeidian area as the research object, using UAV image technology and hyperspectral image acquisition sensors to complete the wheat leaf area index, leaf biomass, Caofeidian area, the determination of growth parameters such as chlorophyll content and leaf nitrogen content was used to construct a PLSR model of wheat growth parameters. the determination coefficient(R2) and root mean square error(RMSE) were used to comprehensively evaluate and verify the PLSR model. Finally, by comparing the measured value of the wheat growth parameter CGMI and the predicted value of the wheat growth parameter PLSR model, it is verified that the remote sensing monitoring of wheat growth based on the UAV image technology has high monitoring accuracy, which has the effect of achieving precision agriculture and improving agricultural production efficiency Very important theoretical guiding significance.
Keywords:UAV  remote sensing monitoring  growth parameters  CGMI  PLSR
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