|Table of Contents|

Quantitative Retrieval of Chlorophyll Content in Leaves of Various Fruits and Leaves Based on Wavelet Technology

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2022年13
Page:
8-15
Research Field:
Publishing date:

Info

Title:
Quantitative Retrieval of Chlorophyll Content in Leaves of Various Fruits and Leaves Based on Wavelet Technology
Author(s):
WANG Yancang1; 2; 3; 4; SHI Jixiang5; ZHANG Liang1; GU Xiaohe6; WANG Jing1; LIN Jialu1
(1.North China Institute of Aerospace Engineering,Langfang,Hebei 065000;2.Shijiazhuang Tiedao University，Shijiazhuang,Hebei 050043；3.National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology，Langfang,Hebei 065000；4.Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province，Langfang,Hebei 065000；5.Shenzhen Interg...
Keywords:
hyperspectral; wavelet technology; mathematical transformation; fruit trees
PACS:
-
DOI:
10.11937/bfyy.20214811
Abstract:
Taking the canopy leaves of ‘Red Leaf Green Peach’‘Big Venus Hawthorn’ and ‘Red Persimmon Tree’ as experimental materials，the hyperspectral data and corresponding SPAD data were obtained by field experiment.The traditional mathematical transform and wavelet transform were used to process and analyze the spectral data，and the partial least square algorithm was used to construct the estimation model of chlorophyll content，the method of accurately detecting the chlorophyll content of many kinds of fruit trees was studied,the law of the separation of spectral information by wavelet technology was analyzed,in order to provide reference for the fruit tree management.The results showed that,1) wavelet technology could redistribute the absorption characteristics of different intensities，and the spectral absorption characteristics were gradually transferred from low-frequency information to high-frequency information，and the intensity of the absorption characteristics of high-frequency information content increases with the increase of decomposition scale.2) Compared with the original spectrum，mathematical transformation wavelet technology could significantly enhance the sensitivity of spectral data to chlorophyll content，and the maximum R2 of the two and chlorophyll content could reach 0.879 (located at 479 nm of H8).3) In the chlorophyll content estimation model based on spectral construction，the model constructed by wavelet technology H6 had the highest accuracy and was the optimal model，and its validation sample R2=0.941，RMSE=0.227，RPD=4.019，which indicates that the accurate detection of chlorophyll content in various fruit leaves by spectral technology was feasible and accurate.

References:

[1]潘瑞炽，王小菁，李娘辉.植物生理学[M].北京:高等教育出版社，2018.[2]王刚，侍瑞，努尔尼萨，等.氮磷钾施肥配比对骏枣叶绿素荧光参数的影响[J].西北农林科技大学学报(自然科学版)，2012(9):187-194.[3]JIAO L，DING H，WANG L，et al.Bisphenol a effects on the chlorophyll contents in soybean at different growth stages[J].Environmental Pollution，2017，223:426-434.[4]何勇，彭继宇，刘飞，等.基于光谱和成像技术的作物养分生理信息快速检测研究进展[J].农业工程学报，2015，31(3):174-189.[5]STEIDLE N，ANTONIO J，LOPES D C，et al.Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves[J].Biosystems Engineering，2017，155:124-133.[6]冯海宽,杨福芹,杨贵军,等.基于特征光谱参数的苹果叶片叶绿素含量估算[J].农业工程学报,2018,34(6):182-188.[7]LIAO Q，WANG J，YANG G，et al.Comparison of spectral indices and wavelet transform for estimating chlorophyll content of maize from hyperspectral reflectance[J].Journal of Applied Remote Sensing，2013，7(1):073575.[8]CHEN X，LI X H，ZHOU Y H，et al.Chlorophyll content prediction for sugarcane leaves based on spectral reflectance with PCA and BP neural network algorithm[J].Computing Technology & Automation，2017，36(1):36-39.[9]FEILHAUER H，ASNER G P，MARTIN R E.Multi-method ensemble selection of spectral bands related to leaf biochemistry[J].Remote Sensing of Environment，2015，164:57-65.[10]孙奇,焦全军,戴华阳.基于光谱指数的不同叶倾角分布下玉米冠层叶绿素含量反演[J].光谱学与光谱分析,2019,39(7):2257-2263.[11]梁爽,赵庚星,朱西存.苹果树叶片叶绿素含量高光谱估测模型研究[J].光谱学与光谱分析,2012,32(5):1367-1370.[12]岳学军,凌康杰,洪添胜,等.基于高光谱图像的龙眼叶片叶绿素含量分布模型[J].农业机械学报,2018,49(8):18-25.[13]王延仓,杨秀峰,赵起超,等.二进制小波技术定量反演北方潮土土壤有机质含量[J].光谱学与光谱分析,2019,39(9):2855-2861.[14]王延仓,金永涛,王晓宁,等.传统光谱变换与连续小波耦合定量反演潮土有机质含量[J].光谱学与光谱分析，2018，38(8)：2571-2577.[15]李辰,王延仓,李旭青,等.基于小波技术的冬小麦植株组分含水率估测模型[J].农业机械学报,2021,52(3):193-201.[16]ABDOLMALEKI M，FATHIANPOUR N，TABAEI M.Evaluating the performance of the wavelet transform in extracting spectral alteration features from hyperspectral images[J].International Journal of Remote Sensing，2018，39（20）:6076-6094.

Memo

Memo:
-
Last Update: 2022-08-29