[1]郭松,常庆瑞,郑智康,等.基于无人机高光谱影像的玉米叶绿素含量估测[J].江苏农业学报,2022,38(04):976-984.[doi:doi:10.3969/j.issn.1000-4440.2022.04.014]
　GUO Song,CHANG Qing-rui,ZHENG Zhi-kang,et al.Estimation of maize chlorophyll content based on unmanned aerial vehicle (UAV) hyperspectral images[J].,2022,38(04):976-984.[doi:doi:10.3969/j.issn.1000-4440.2022.04.014]
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基于无人机高光谱影像的玉米叶绿素含量估测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
38
期数:
2022年04期
页码:
976-984
栏目:
农业信息工程
出版日期:
2022-08-31

文章信息/Info

Title:
Estimation of maize chlorophyll content based on unmanned aerial vehicle (UAV) hyperspectral images
作者:
郭松; 常庆瑞; 郑智康; 蒋丹垚; 高一帆; 宋子怡; 姜时雨
（西北农林科技大学资源环境学院，陕西杨凌712100）
Author(s):
GUO Song; CHANG Qing-rui; ZHENG Zhi-kang; JIANG Dan-yao; GAO Yi-fan; SONG Zi-yi; JIANG Shi-yu
（College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China）
关键词:
玉米; 叶绿素含量; 无人机; 连续投影算法; 核极限学习机回归模型
Keywords:
maize; chlorophyll content; unmanned aerial vehicle (UAV); successive projections algorithm; kernel extreme learning machine regression model
分类号:
S127；S513
DOI:
doi:10.3969/j.issn.1000-4440.2022.04.014
文献标志码:
A
摘要:
为实现玉米叶绿素含量的快速估测，使用低空无人机搭载S185高光谱相机获取关中地区抽雄期玉米冠层高光谱影像，并在地面同步测定采样点叶绿素含量（Chl值），以原始光谱和一阶导数光谱为基础构建模型，分别通过任意2波段组合以及连续投影算法提取单因素建模参数和多因素建模参数，对比分析各类模型（单因素回归模型、多元线性回归模型和狮群算法优化的核极限学习机模型）对抽雄期玉米Chl值的预测能力。结果表明，原始光谱上Chl值的敏感波段主要集中于绿光波段和近红外波段，一阶导数光谱上Chl值的敏感波段主要集中于近红外波段。原始光谱和一阶导数光谱中的最优单因素建模参数均为差值光谱指数（DSI），相关系数分别为0.71、0.68，连续投影算法筛选的多因素建模参数分别为14个、8个。原始光谱和一阶导数光谱下均为多因素模型估测效果优于单因素模型，机器学习算法优于传统回归算法，其中基于原始光谱的狮群算法优化的核极限学习机（LSO-KELM）回归模型是此次研究中的最优模型，具有较好的填图精度，其建模决定系数（R2）和验证R2分别为0.73、0.70，平均相对误差（MRE）分别为3.56%、3.53%。说明结合无人机高光谱影像与LSO-KELM可较好地估测田间抽雄期玉米冠层的叶绿素含量。
Abstract:
In order to achieve rapid estimation of maize chlorophyll content, a low-altitude unmanned aerial vehicle (UAV) equipped with S185 hyperspectral camera was used to obtain the hyperspectral images of maize canopy during the tasseling period in Guanzhong area, and the chlorophyll content (Chl value) of sampling point was measured synchronously on the ground. The models were built based on the primary spectra and first derivative spectra. Single-factor modeling parameters and multi-factor modeling parameters were extracted by any two bands combination and successive projections algorithm, respectively, and the prediction ability of various models (single-factor regression model, multiple linear regression model and kernel extreme learning machine model combined with lion swarm algorithm) on Chl value of maize at tasseling stage was compared and analyzed. The results showed that the sensitive bands of Chl value in the primary spectra were mainly concentrated in the green band and the near infrared band, and the sensitive bands of Chl value in the first derivative spectra were mainly concentrated in the near infrared band. The optimal single-factor modeling parameters in the primary spectra and the first derivative spectra were difference spectral index (DSI), and the correlation coefficients were 0.71 and 0.68, respectively. The number of multi-factor modeling parameters selected by the successive projections algorithm was fourteen and eight, respectively. In both the primary spectrum and the first derivative spectrum, the estimation effect of the multi-factor model was better than that of the single-factor model, and the machine learning regression algorithm was better than the traditional regression algorithm. In this study, the kernel extreme learning machine combined with the lion swarm algorithm (LSO-KELM) regression model was the optimal model, which had good mapping accuracy. The modeling and validation determination coefficient (R2) were 0.73 and 0.70, respectively, and the mean relative errors (MRE) were 3.56% and 3.53%, respectively. The research results demonstrate that the combination of UAV hyperspectral images and LSO-KELM can better estimate the chlorophyll content of maize canopy during the tasseling period in the field.

参考文献/References:

[1]袁小康，周广胜，王秋玲，等. 不同灌溉量夏玉米叶绿素含量的高光谱特征及其反演[J]. 生态学报, 2021, 41(2): 543-552.
[2]陈澜，常庆瑞，高一帆，等. 猕猴桃叶片叶绿素含量高光谱估算模型研究[J]. 西北农林科技大学学报(自然科学版), 2020, 48(6): 79-89，98.
[3]努尔凯麦尔·木拉提，杨亚杰，帕尔哈提·阿布都克日木，等. 小麦叶绿素含量测定方法比较[J]. 江苏农业科学, 2021, 49(9): 156-159.
[4]周静，胡忠军，李培培，等. 基于叶绿素a测定的分光光度法与bbe法比较:以千岛湖为例[J]. 水生态学杂志, 2013, 34(2): 80-84.
[5]崔小涛，常庆瑞，屈春燕，等. 基于高光谱和MLSR-GA-BP神经网络模型油菜叶片SPAD值遥感估算[J]. 东北农业大学学报, 2020, 51(8): 74-84.
[6]王婷婷，常庆瑞，刘梦云，等. 基于Dualex植物多酚-叶绿素仪的冬小麦叶绿素含量高光谱估算[J]. 麦类作物学报, 2019, 39(5): 595-604.
[7]王伟东，常庆瑞，王玉娜. 基于UHD185成像光谱仪的冬小麦叶绿素监测[J]. 干旱区研究, 2020, 37(5): 1362-1369.
[8]廖小罕，周成虎，苏奋振，等. 无人机遥感众创时代[J]. 地球信息科学学报, 2016, 18(11): 1439-1447.
[9]黄耀欢，李中华，朱海涛. 作物胁迫无人机遥感监测研究评述[J]. 地球信息科学学报, 2019, 21(4): 512-523.
[10]田明璐，班松涛，常庆瑞，等. 基于低空无人机成像光谱仪影像估算棉花叶面积指数[J]. 农业工程学报, 2016, 32(21): 102-108.
[11]陈鹏飞，梁飞. 基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究[J]. 中国农业科学, 2019, 52(13): 2220-2229.
[12]陈晓凯，李粉玲，王玉娜，等. 无人机高光谱遥感估算冬小麦叶面积指数[J]. 农业工程学报, 2020, 36(22): 40-49.
[13]陶惠林，徐良骥，冯海宽，等. 基于无人机高光谱长势指标的冬小麦长势监测[J]. 农业机械学报, 2020, 51(2): 180-191.
[14]SINGHAL G, BANSOD B, MATHEW L, et al. Estimation of leaf chlorophyll concentration in turmeric ( Curcuma longa ) using high-resolution unmanned aerial vehicle imagery based on kernel ridge regression[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(7): 1111-1112.
[15]COLORADO J D, CERA-BORNACELLI N, CALDAS J S, et al. Estimation of nitrogen in rice crops from UAV-captured images[J]. Remote Sensing, 2020, 12(20): 3396.
[16]陈妮娜，纪瑞鹏，贾庆宇，等. 关键发育期干旱对春玉米产量及籽粒品质的影响[J]. 生态学杂志, 2021, 40(6): 1687-1694.
[17]陈鹏飞，李刚，石雅娇，等. 一款无人机高光谱传感器的验证及其在玉米叶面积指数反演中的应用[J]. 中国农业科学, 2018, 51(8): 1464-1474.
[18]GAUTAM K A, SINGH V, BECTOR V, et al. Trend analysis of vegetation indices using spectroradiometer at different growth stages of cotton[J]. Agricultural Mechanization in Asia Africa & Latin America, 2018, 49(1):63-66.
[19]李传华，赵军，师银芳，等. 基于变异系数的植被NPP人为影响定量研究——以石羊河流域为例[J]. 生态学报, 2016, 36(13): 4034-4044.
[20]SOARES S, GOMES A, FILHO A, et al. The successive projections algorithm[J]. TrAC Trends in Analytical Chemistry, 2013, 42:84-97.
[21]HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1-3):489-501.
[22]刘生建，杨艳，周永权. 一种群体智能算法——狮群算法[J]. 模式识别与人工智能, 2018, 31(5): 431-441.
[23]刘秀英，刘晨洲，吴姗微，等. 玉米叶片花青素相对含量高光谱遥感反演[J]. 遥感信息, 2018, 33(6): 1-8.
[24]杨婧，廖桂平，刘凡，等. 基于高光谱成像技术的油菜叶片叶绿素含量预测[J]. 中国农业科技导报, 2020, 22(5): 86-96.
[25]YANN L, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[26]聂立新，张天侠，赵波. 粒子群算法优化双核支持向量机及应用[J]. 振动、测试与诊断, 2014, 34(3): 565-569.
[27]WANG L, CHANG Q R, YANG J, et al. Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments[J]. PLoS One, 2018, 13(12): e0207624.
[28]LEE H, WANG J, LEBLON B. Using linear regression, random forests, and support vector machine with unmanned aerial vehicle multispectral images to predict canopy nitrogen weight in corn[J]. Remote Sensing, 2020, 12(13):2071.

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备注/Memo

备注/Memo:
收稿日期：2021-10-25基金项目：国家高技术研究发展计划（“863”计划）项目（2013AA102401）作者简介：郭松（1996-），男，贵州贵阳人，硕士研究生，主要从事土地资源和空间信息技术研究。(E-mail)1185716519@qq.com通讯作者：常庆瑞，(E-mail)changqr@nwsuaf.edu.cn
更新日期/Last Update: 2022-09-06