乔浪,张智勇,陈龙胜,孙红,李莉,李民赞.基于无人机图像的玉米冠层叶绿素含量检测与分布研究[J].农业机械学报,2019,50(Supp):182-186.
QIAO Lang,ZHANG Zhiyong,CHEN Longsheng,SUN Hong,LI Li,LI Minzan.Chlorophyll Content Detection and Distribution Research of Maize Canopy Based on UAV Image[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(Supp):182-186.
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基于无人机图像的玉米冠层叶绿素含量检测与分布研究   [下载全文]
Chlorophyll Content Detection and Distribution Research of Maize Canopy Based on UAV Image   [Download Pdf][in English]
投稿时间:2019-04-10  
DOI:10.6041/j.issn.1000-1298.2019.S0.028
中文关键词:  玉米冠层  叶绿素  无人机遥感技术  BP神经网络  可视化分布
基金项目:国家重点研发计划项目(2018YFD0300505-1)、中国农业大学研究生实践教学基地建设项目(ZYXW037)和中国农业大学研究生课程建设项目(HJ2019029、YW2019018)
作者单位
乔浪 中国农业大学 
张智勇 中国农业大学 
陈龙胜 中国农业大学 
孙红 中国农业大学 
李莉 中国农业大学 
李民赞 中国农业大学 
中文摘要:为了快速、无损地获取大田作物叶绿素含量空间分布,基于无人机遥感技术研究了大田玉米冠层叶绿素含量检测及分布图绘制方法。利用无人机遥感技术采集了150幅大田玉米的航拍图像,并通过Pix4dmapper软件对其进行了拼接;在实验田中,等距获取80株玉米叶片样本,通过化学法萃取叶绿素,并使用分光光度计测量叶绿素含量,形成了基础数据源。在数据处理方面,采用ArcGIS软件对样本点的POS(Position and orientation system)数据与无人机图像进行匹配;对无人机拍摄的RGB图像,首先进行R、G、B三通道分量值提取,构建了绿红比值、绿红差值、归一化红绿差值、超绿等10种颜色特征,并计算了均值、标准偏差、平滑度、三阶矩等6种纹理特征,然后建立了基于BP(Back propagation)神经网络的玉米冠层叶绿素含量检测模型。实验结果表明,基于BP神经网络的玉米冠层叶绿素含量检测模型的均方根误差RMSE为4.4659mg/L,决定系数R2为0.7246。通过BP神经网络检测模型计算出大田玉米图像每个像素点的叶绿素含量,基于伪彩色技术绘制大田玉米叶绿素含量可视化分布图,分析田间玉米冠层叶绿素含量分布图可以直观区分田间道路与冠层区域,显示地块叶绿素分布差异。通过无损检测大田玉米冠层叶绿素含量及叶绿素分布可视化,可为田间作物长势评价和精细化管理提供技术支持。
QIAO Lang  ZHANG Zhiyong  CHEN Longsheng  SUN Hong  LI Li  LI Minzan
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:maize canopy  chlorophyll  UAV remote sensing technology  BP neural network  visual distribution
Abstract:Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and non destructively, the chlorophyll content detection and distribution map drawing method of maize canopy were carried out based on UAV remote sensing technology. Firstly, the aerial images of 150 maize plots were collected by UAV mounted camera and spliced by Pix4dmapper software. Totally 80 maize leaves were sampled in the experimental field. They were processed following chemical extraction and spectrophotometer measurement to obtain the chlorophyll content value. The images and chlorophyll data were used to form the underlying data source. In the aspects of data processing, the position and orientation system (POS) data of the sample points were matched with the images of the UAV using ArcGIS software. For the RGB images captured by the drone, the three channel component values of R, G and B were firstly extracted. The color feature parameters were calculated such as green red difference, normalized red green difference, super green, and so on. In addition, six kinds of texture features were calculated, including mean, standard deviation, smoothness and third order moment. The error back propagation neural network was used to build chlorophyll detection model for maize canopy leaves. The experimental results were as follows: the root mean square error (RMSE) of the maize canopy chlorophyll content detecting model based on BP neural network was 4.4659mg/L, and the coefficient of determination R2 was 0.7246. The chlorophyll content of each pixel in the field canopy image was calculated. The visual distribution map of chlorophyll content in field maize canopy was drawn based on pseudo color technique. The chlorophyll content distribution map of field maize canopy could be used to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. By non destructively detecting the chlorophyll content and chlorophyll distribution of canopy corn canopy, it could provide a support for field crop growth evaluation and precision management.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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