Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Position

Proceedings, 6th IFAC Conference on Bio-Robotics Proceedings, IFAC Conference on Bio-Robotics Beijing, China,6th July 13-15, 2018 Proceedings, 6th IFAC Conference on Bio-Robotics Proceedings, 6th IFAC Conference onAvailable Bio-Robotics Proceedings, IFAC Conference on Bio-Robotics Beijing, China,6th July 13-15, 2018 online at www.sciencedirect.com Beijing, China, July 13-15, 2018 Beijing, China, July 13-15, 2018 Proceedings, IFAC Conference on Bio-Robotics Beijing, China,6th July 13-15, 2018 Beijing, China, July 13-15, 2018

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IFAC PapersOnLine 51-17 (2018) 602–606

Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Estimation of Chlorophyll Content inPosition Potato Leaves Based on Spectral Red Edge Position Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Position Position Tao Zheng *, Ning Liu *, Li Wu *, Minzan Li *, Hong Sun *, Qin Zhang**, Jingzhu Wu *** Position Tao Zheng *, Ning Liu *, Li Wu *, Minzan Li *, Hong Sun *, Qin Zhang**, Jingzhu Wu *** Tao Zheng *, Ning Liu *, Li Wu *, Minzan Li *, Hong Sun *, Qin Zhang**, Jingzhu Wu *** Position Tao Li Tao Zheng Zheng *, *, Ning Ning Liu Liu *, *, Li Li Wu Wu *, *, Minzan Minzan Li *, *, Hong Hong Sun Sun *, *, Qin Qin Zhang**, Zhang**, Jingzhu Jingzhu Wu Wu *** ***

Tao Zheng *, Ning Liu *,Agriculture Li Wu *, Minzan *, Hong Sun *, QinMinistry Zhang**, Jingzhu Wu *** Agricultural * Key Laboratory of Modern Precision System Li Integration Research, of Education, China ** Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural Key of Agriculture Integration Ministry of Beijing 100083 China System (Tel: 86-10-62737838; e-mail: [email protected]) Key Laboratory Laboratory University, of Modern Modern Precision Precision Agriculture System Integration Research, Research, Ministry of Education, Education, China China Agricultural Agricultural ** Key Laboratory of Modern Precision Agriculture Integration Research, Ministry of Education, China Agricultural University, Beijing 100083 100083 China System (Tel: 86-10-62737838; 86-10-62737838; e-mail: [email protected]) University, Beijing China (Tel: e-mail: [email protected]) Center forUniversity, Automated Agricultural System, Washington State University, Prosser, WA 99350 USA * Key**Laboratory ofPrecision Modern & Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083 China (Tel: 86-10-62737838; e-mail: [email protected]) Beijing 100083 China (Tel:System, 86-10-62737838; e-mail: [email protected]) ** Center for Precision & Automated Agricultural Washington State University, Prosser, WA 99350 USA Center for Precision & Automated Agricultural System, Washington State University, Prosser, WA 99350 USA *** ** Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing University, Beijing 100083 China (Tel: 86-10-62737838; e-mail: [email protected]) ** Center for Precision & Automated Agricultural System, Washington State University, Prosser, WA 99350 USA ** Center forLaboratory Precision & Automated Agricultural System, Washington State University, Prosser, WA 99350 USA *** Beijing Key of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing *** Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048 ** Center Precision & Agricultural System, Washington University, 99350 USA *** Beijing Beijing KeyforLaboratory Laboratory of Automated Big Data Data Technology Technology for Food China Safety, BeijingState Technology andProsser, BusinessWA University, Beijing *** Key of Big for Food Safety, Beijing Technology and Business University, Beijing 100048 China 100048 China *** Beijing Key Laboratory of Big Data Technology for Food China Safety, Beijing Technology and Business University, Beijing 100048 China 100048 100048 China Abstract: In order to indicate the growth of potato crops and provide guidance for precision Abstract: In order to indicate the growth of potato crops and provide guidance for precision Abstract: order the growth potato crops provide guidance for management, testing chlorophyll content and mapping were Abstract: In In non-destructive order to to indicate indicate the techniques growth of of for potato crops and and provide guidance methods for precision precision Abstract: In order to indicate the growth of potato crops and provide guidance for precision management, non-destructive testing techniques for chlorophyll content and mapping methods were management, non-destructive testing chlorophyll content and mapping were studied for In potato In this paper, the hyperspectral imaging technique used tomethods estimate the Abstract: ordercrops. to indicate the techniques growth of for potato crops and provide guidance for precision management, non-destructive testing techniques for chlorophyll content andwas mapping methods were management, non-destructive testing techniques for chlorophyll content and mapping methods were studied for potato crops. In this paper, the hyperspectral imaging technique was used to estimate the studied for potato crops. In this paper, the hyperspectral imaging technique was used to estimate the chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral management, non-destructive testing techniques for chlorophyll content and mapping methods were studied for for potato potato crops. crops. In In this this paper, paper, the the hyperspectral hyperspectral imaging imaging technique technique was was used used to to estimate estimate the the studied chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral of interesting chlorophyll content index and help to chlorophyll distribution potato leaf. The images of potatocrops. leavesIn were collected andhyperspectral divided into imaging 400 regions of (ROI). Meanwhile, studied for65potato this paper, the technique was used estimate the chlorophyll content index and help to describe describe chlorophyll distribution potato leaf. Thetohyperspectral hyperspectral chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral images of 65 potato leaves were collected and divided into 400 regions interesting (ROI). Meanwhile, images of potato leaves were collected and divided into regions of (ROI). Meanwhile, After extracting and leaf. calculating the average the SPAD values of index these ROI samples were measured. chlorophyll content and help to describe chlorophyll distribution potato The hyperspectral images of 65 65 potato leaves400 were collected and divided into 400 400 regions of interesting interesting (ROI). Meanwhile, images of 65 potato leaves were collected divided into 400 regions of interesting (ROI). Meanwhile, After extracting and calculating calculating the average average the SPAD values of these these 400 ROI samplesand were measured. After extracting and the the SPAD values of 400 ROI samples were measured. was leaf spectrum of the chlorophyll measurement area, the optimal red edge position images of 65 potato leaves were collected and divided into 400 regions of interesting (ROI). Meanwhile, After extracting and calculating the average the SPAD values of these 400 ROI samples were measured. After extracting and calculating the between average the SPAD values ofthe these 400 ROI samples were measured. was between leaf spectrum of chlorophyll measurement area, the optimal red edge position was between leaf spectrum of the chlorophyll measurement area, the optimal red edge position an estimating model for chlorophyll content in potato leaves approximately 702 nm and 706 nm. Thus, After extracting and calculating the average the SPAD values of these 400 ROI samples were measured. was between leaf spectrum of the chlorophyll measurement area, the optimal red edge position between leaf spectrum 702 of the chlorophyll measurement area, the optimal red edge content positionin was an estimating model for chlorophyll potato leaves approximately nm and 706 nm. Thus, an estimating model for chlorophyll content in potato approximately nm and 706 nm. Thus, a leaves set of was established based on red edge position. The determination coefficient was 0.8682. was between leaf spectrum 702 of the chlorophyll measurement area, the optimal red edge positionFinally, an estimating model for chlorophyll content in potato leaves approximately 702 nm and 706 nm. Thus, an estimating model for chlorophyll content in potato leaves approximately 702 nm and 706 nm. Thus, Finally, a set of was established based on red edge position. The determination coefficient was 0.8682. set was established based edge determination was 0.8682. It mapping software for rapid detection of chlorophyll distribution potato leaves wasFinally, anThe estimating model coefficient for in chlorophyll content in developed. potatoaaa leaves approximately 702 nm on andred 706 nm.position. Thus, Finally, set of was established based on red edge position. The determination coefficient was 0.8682. Finally, set of of was established based on red edge position. The determination coefficient was 0.8682. It mapping software for rapid detection of chlorophyll distribution in potato leaves was developed. It mapping for rapid of chlorophyll distribution potato was developed. provides asoftware method for the estimation of chlorophyll distribution incoefficient thein future. Finally, a set of was established based on red detection edge position. The determination wasleaves 0.8682. It mapping software for rapid detection of chlorophyll distribution in potato leaves was developed. It mapping software for rapid detection of chlorophyll distribution in potato leaves was developed. provides a method for the estimation of chlorophyll distribution in the future. provides a method for the estimation of chlorophyll distribution in the future. It mapping software for rapid detection of chlorophyll distribution in potato leaves was developed. provides aa method for the estimation of chlorophyll distribution in the future. provides method for the estimation of chlorophyll distribution in the future. © 2018, IFAC (International Federation Automatic Control)imaging, Hosting by Elsevier Ltd. All rights reserved. Keywords: chlorophyll content, potatoofof crops, hyperspectral red edge, mapping software.  provides a method for the estimation chlorophyll distribution in the future. Keywords: chlorophyll content, potato crops, hyperspectral imaging, red edge, mapping software.  Keywords: chlorophyll content, potato crops, hyperspectral imaging, red edge, mapping software. Keywords: Keywords: chlorophyll chlorophyll content, content, potato potato crops, crops, hyperspectral hyperspectral imaging, imaging, red red edge, edge, mapping mapping software. software.  LAI and CCD of rice and wheat, and the determination Keywords: chlorophyll content, potato crops, hyperspectral redofedge, software. LAI imaging, and CCD CCD ricemapping and wheat, wheat, and the the determination determination 1. INTRODUCTION LAI and ofmore rice and and coefficients were than 0.85. These results provided an 1. INTRODUCTION LAI and CCD of rice and wheat, and the LAI and CCD ofmore rice than and 0.85. wheat, and results the determination determination 1. coefficients were These provided an an 1. INTRODUCTION INTRODUCTION 1. INTRODUCTION coefficients were more than 0.85. These results provided insight for monitoring the dynamics of crop and scientific LAI and CCD of rice and wheat, and the determination coefficients were more than 0.85. These results provided an Chlorophyll is the most important material to absorb light coefficients were more the thandynamics 0.85. These provided an insight for for monitoring monitoring of results crop and and scientific 1. INTRODUCTION Chlorophyll is the most important material to absorb light insight the dynamics of crop scientific of more agricultural production under different coefficients were thandynamics 0.85. These provided an insight for the of crop and scientific Chlorophyll is the important material to absorb energy, which directly affects the light energy utilization of management Chlorophyll is the most important material to absorb light insight for monitoring monitoring the dynamics of results crop and scientific Chlorophyll is directly the most mostaffects important material to utilization absorb light light management of agricultural agricultural production under different energy, which the light energy of management of production under different growth environment (Feng et al.,2010). insight for monitoring the dynamics of crop and scientific management of agricultural production under different energy, which directly affects the light energy utilization of crop photosynthesis. The chlorophyll content of crops has a Chlorophyll is directly the mostaffects important material to utilization absorb light energy, which the energy of management of agricultural production under different energy, which directly affects the light light energy growth environment (Feng et al.,2010). al.,2010). crop photosynthesis. photosynthesis. The chlorophyll content ofutilization crops has hasofaa growth (Feng of agricultural production under different growth environment (Feng et crop The chlorophyll content of crops good correlation with their photosynthetic capacity, growth environment environment (Feng et et al.,2010). al.,2010). energy, which directly affects the light energyof utilization ofaa management crop photosynthesis. The chlorophyll content crops has crop photosynthesis. The chlorophyll content of crops has general, methods foret al.,2010). estimating crop biochemical good correlation correlation with with their their photosynthetic photosynthetic capacity, capacity, In environment (Feng good In general, general, methods for estimating estimating crop crop biochemical biochemical developmental stageswith and nitrogen And,of it has become crop photosynthesis. The chlorophyll content crops has a growth good correlation their photosynthetic capacity, good correlation with their status. photosynthetic capacity, In methods for parameters using hyperspectral data include multivariate developmental stages and nitrogen status. And, it has become In general, methods for estimating crop In general, methods for estimating crop biochemical biochemical developmental stages and nitrogen status. And, it has become parameters using hyperspectral data include multivariate an effective means to evaluate crop growth. The reflectance good correlation with their photosynthetic capacity, developmental stages and nitrogen status. And, it has become developmental stages and nitrogen status. And, it has become In parameters using hyperspectral data include multivariate statistical methods, analysis methods based on an effective means to evaluate crop growth. The reflectance general,analysis methods for estimating crop biochemical parameters using hyperspectral data include multivariate parameters using hyperspectral data include multivariate an effective means to evaluate crop growth. The reflectance statistical analysis methods, analysis methods based on on spectrum of crop leaves is mainly affected by crop pigments developmental stages and nitrogen status. And, it has become an effective means to evaluate crop growth. The reflectance an effective meansleaves to evaluate crop growth.byThe statistical analysis methods, analysis methodstransmission based feature spectral variables, and include optical spectrum of crop crop is mainly mainly affected cropreflectance pigments parameters usingposition hyperspectral data multivariate statistical analysis methods, analysis methods based on statistical analysis methods, analysis methods based on spectrum of leaves is affected by crop pigments feature spectral position variables, and optical transmission in the visible light range (Dordas, 2017, Zhao et al., 2016). an effective means to evaluate crop growth. The reflectance spectrum of crop leaves is mainly affected by crop pigments spectrum of crop mainly affected by crop pigments feature spectral spectral position variables, and optical optical transmission methods. The analysis technique based on the in the the visible lightleaves rangeis(Dordas, (Dordas, 2017, Zhao Zhao et al., al., 2016). model statistical analysis methods, analysis methods based on feature position variables, and transmission variables, and optical transmission in range 2017, et 2016). model spectral methods.position The analysis analysis technique based on the the However, islight mainly affected the internal structure of feature spectrum ofit crop leaves is(Dordas, mainlybyaffected by crop pigments in the visible light range 2017, Zhao et al., 2016). in the visible visible light range (Dordas, 2017, Zhao etstructure al., 2016). model methods. The technique based on characteristic spectral position variable is widely used in the However, it is mainly affected by the internal of feature spectral position variables, and optical transmission model methods. The analysis technique based on model methods. Theposition analysis technique based the However, it is mainly affected the of characteristic spectral variable is widely widely usedonin in the leaves in the region.by Therefore, it can be used as in the visible range (Dordas, 2017, Zhao etstructure al., 2016). However, it near-infrared islight mainly affected by the internal internal structure of research, However, it is mainly affected the internal of characteristic spectral position variable is used including theposition position of technique red edge and the position leaves in in the the near-infrared region.by Therefore, it can canstructure be used used as as model methods. The analysis based onin characteristic spectral variable is widely used the characteristic spectral position variable is widely used in the leaves near-infrared region. Therefore, it be research, including the position of red edge and the position aleaves reflection spectrum of the canopy layer and leaves to However, it near-infrared is mainly affected the internal of research, including the position of red edge and the position leaves in in the the near-infrared region.by Therefore, it can canstructure be used used as Therefore, green peak. In the spectrum, the a reflection reflection spectrum of of region. the canopy canopy layerit and and be leaves as to of characteristic spectral position variable isregion widely used in red the research, including the position of edge and the position research, including thecrop position of red redthe and from the position spectrum the layer leaves to of green green peak. peak. In the the crop spectrum, theedge region from the red red itsnear-infrared biochemical parameters, leaves in the region. Therefore, itespecially can be usedthe as aaaestimate reflection spectrum of the canopy layer and leaves to reflection spectrum of the canopy layer and leaves to band of In crop spectrum, the region from the to the near infrared band contains important estimate its biochemical parameters, especially the research, including the position of red edge and the position of green peak. In the crop spectrum, the region from the red of green peak. In the crop spectrum, the region from the red estimate itscontent biochemical parameters, especially the band to to the the near near infrared infrared band band contains contains important important chlorophyll (Feng et al., 2018,layer Meskinivishkaee et band aestimate reflectionits spectrum of the canopy and leaves the to biochemical parameters, especially estimate biochemical parameters, especially the information. isspectrum, calledband the red edge, and the chlorophyllitscontent content (Feng et et al., al., 2018, Meskinivishkaee Meskinivishkaee et of greento Innear thearea crop the contains region from the red band the infrared important band topeak. theThis near infrared band contains important chlorophyll (Feng 2018, et information. This area is called the red edge, and the the al., 2015). estimate its biochemical parameters, especially the chlorophyll content (Feng et al., 2018, Meskinivishkaee et chlorophyll content (Feng et al., 2018, Meskinivishkaee et wavelength information. This area is called the red edge, and al., 2015). position is infrared generally between 660 nmimportant and 770 band to the neararea band contains information. This is called the red edge, the information. This area is called the red edge, and the al., 2015). wavelength position is generally between 660 nm and 770 chlorophyll content (Feng et al., 2018, Meskinivishkaee et al., 2015). al., 2015). wavelength position is generally generally between 660 nm and 770 It is position caused by vegetation's absorption of information. This area is calledbetween thesharp red660 edge, and 770 the wavelength is nm The use of hyperspectral imaging techniques to detect nm. wavelength is generally between nm and 770 nm. It It is is position caused by by vegetation's sharp660absorption absorption of al., The2015). use of of hyperspectral hyperspectral imaging imaging techniques techniques to to detect detect chlorophyll nm. caused vegetation's sharp of in the red light band and the multiple scattering wavelength position is generally between 660 nm and 770 nm. It is caused by vegetation's sharp absorption of The use changes chlorophyllimaging content techniques has been studied for nm. The use of hyperspectral to It is in caused bylight vegetation's sharp absorption of The use in ofcrop hyperspectral imaging techniques to detect detect chlorophyll the red band and the multiple scattering changes in crop chlorophyll content has been studied for chlorophyll in the redbylight light band andresulting the multiple scattering of light near-infrared band, in a steep and It in is the caused vegetation's sharp absorption of chlorophyll in the red band and the multiple scattering changes in chlorophyll content has been for many use years (Liang et al., 2012, Simko al., studied 2015). Pan The hyperspectral imaging techniques to detect changes in crop chlorophyll content has been studied for chlorophyll in the red light band andresulting the multiple scattering changes inofcrop crop chlorophyll content haset for nm. of light light in in the the near-infrared band, in aa steep steep and many years years (Liang et al., al., 2012, 2012, Simko etbeen al., studied 2015). Pan Pan of near-infrared band, resulting in and nearly straight bevel edge (Yang et al., 2015). This particular chlorophyll in the red light band and the multiple scattering of light in the near-infrared band, resulting in a steep and many (Liang et Simko et al., 2015). measured hyperspectral reflectance of et apple canopy changes inthecrop chlorophyll content has been studied for of many years (Liang et Simko al., 2015). Pan lightstraight in the bevel near-infrared band, resulting a steep and many years et al., al., 2012, 2012, Simko al., tree 2015). Pan nearly edge (Yang (Yang et al., al., 2015). in This particular measured the(Liang hyperspectral reflectance of et apple tree canopy nearly straight bevel edge et 2015). This particular instraight the reflectance is unique to plants, of light in the near-infrared band, resulting in a steep and nearly bevel edge (Yang et al., 2015). This particular measured the hyperspectral reflectance apple during spring shoots spring growth Thecanopy best nearly straight bevel edgespectrum (Yang etcurve al., 2015). This particular many years et al., 2012, Simkoof et al., tree 2015). Pan form measured the(Liang hyperspectral reflectance ofperiod. apple tree canopy measured the hyperspectral reflectance of apple tree canopy form in the reflectance spectrum curve is unique to plants, during spring spring shoots shoots spring spring growth growth period. period. The The best best and form in the reflectance spectrum curve isbeen unique to plants, scholars atbevel home and abroad have paying high nearly edge (Yang etcurve al., 2015). This particular in the spectrum is unique to plants, during vegetation werespring chosen and ofthe apple form instraight the reflectance reflectance spectrum unique to plants, measured theindices hyperspectral reflectance apple tree during spring shoots growth period. The best during spring shoots spring growth period. Thecanopy best form and scholars scholars at home home and and abroadcurve haveisbeen been paying high vegetation indices were chosen and the apple canopy and at abroad have paying high attention to reflectance it. home Among them, the large slope of the form in the spectrum curve is unique to plants, and scholars at and abroad have been paying high vegetation indices were chosen and the apple canopy chlorophyll content estimation model was established by and scholars at home and abroad have been paying high during spring shoots spring growth period. The best vegetation indices were chosen and the apple canopy vegetation indices were chosen and the apple canopy attention to it. Among them, the large slope of the chlorophyll content content estimation estimation model model was was established established by by wavelength attention to it. Among them, the large slope of the is at defined asand thethem, red edge position. In the study of and scholars home abroad have been paying high attention to it. Among the large slope of the chlorophyll analyzing vegetation index of two-band combination in the attention to it. Among them, the large slope of vegetation indices were chosen and the apple canopy chlorophyll content estimation model was established by chlorophyll content estimation model was established by wavelength wavelength is is defined defined as as the the red red edge edge position. position. In In the the study studythe of analyzing vegetation index of two-band combination in the of Ding, the changes of red edge parameters of tomato leaves analyzing vegetation index of two-band combination in the attention to it. Among them, the large slope of the wavelength is defined as the red edge position. In the study of sensitive region 400~1350 It model provided a theoretical chlorophyll content estimation was established by wavelength analyzing vegetation index of combination in the is defined as theedge red parameters edge position. In the study of analyzing vegetation indexnm. of two-band two-band combination inbasis the Ding, the changes of red of tomato leaves sensitive region 400~1350 nm. It provided a theoretical basis Ding, the the changes of as redthe edge parameters of In tomato leaves different nutritional levels were analyzed. The result wavelength is defined red parameters edge position. the study of Ding, changes of of leaves sensitive 400~1350 It aaa theoretical for rapid region apple tree canopy diagnosis and growth analyzing vegetation indexnm. ofnutrition two-band combination inbasis the under sensitive nm. It provided theoretical basis Ding, changes of red red edge edge of tomato tomato sensitive region 400~1350 nm. It provided provided theoretical basis under the different nutritional levelsparameters were analyzed. analyzed. The leaves result for rapid rapid region apple 400~1350 tree canopy canopy nutrition diagnosis and growth growth under different nutritional levels were The result showed that the red edge position is the best one for Ding, the changes of red edge parameters of tomato leaves under different nutritional levels were analyzed. The result for apple tree nutrition diagnosis and monitoring (Pan400~1350 et al.,2013). Feng measured the and changes of under sensitive nm. It provided a theoretical basis for rapid apple tree canopy nutrition diagnosis growth were isanalyzed. for rapid region apple canopy nutrition diagnosis growth showeddifferent that the thenutritional red edge edgelevels position the best bestThe oneresult for monitoring (Pan tree et al.,2013). al.,2013). Feng measured measured theand changes of diagnosing showed that red position isanalyzed. the one for thethe growth state ofposition tomato toThe statistical under different nutritional levels wereaccording result showed that red edge is the best one for monitoring (Pan et Feng the changes of the canopyapple hyperspectral reflectance, leaf area index (LAI) for rapid tree canopy nutrition diagnosis and growth monitoring (Pan et al.,2013). Feng measured the changes of showed that the red edge position is the best one for monitoring (Pan et al.,2013). Feng measured the changes of diagnosing the growth state of tomato according to statistical the canopy canopy hyperspectral hyperspectral reflectance, reflectance, leaf leaf area area index index (LAI) (LAI) analysis. diagnosing the growth state of tomato according to the statistical Meanwhile, the ofto that red state edgelogarithmic ismodel the best onelinear for diagnosing the growth of tomato statistical the and chlorophyll content density (CCD) of rice and wheat in showed diagnosing thethe growth state ofposition tomato according according statistical monitoring (Pan et al.,2013). Feng measured the changes of the canopy hyperspectral reflectance, leaf area index (LAI) the canopy hyperspectral reflectance, leaf area index (LAI) analysis. Meanwhile, Meanwhile, the logarithmic logarithmic model of ofto the the linear and chlorophyll content density (CCD) of rice and wheat in analysis. the model linear extrapolation had the state bestlogarithmic accuracy and reliability. The diagnosing the growth of tomato according to statistical analysis. Meanwhile, the model of the linear and chlorophyll content density (CCD) of rice and wheat in different growth period. The study confirmed the optimum analysis. Meanwhile, the logarithmic model of the linear the canopy hyperspectral reflectance, leaf area index (LAI) and chlorophyll content density (CCD) of rice and wheat in and chlorophyll (CCD) of rice and wheat in extrapolation extrapolation had had the the best best accuracy accuracy and and reliability. reliability. The The different growth content period. density The study study confirmed the optimum optimum predictive ability enough to develop a monitoring analysis. Meanwhile, the model of the linear had the best accuracy and The different growth period. The confirmed the vegetation indices for estimating LAI and CCDand of optimum rice and extrapolation hadwas thegood bestlogarithmic accuracy and reliability. reliability. The and chlorophyll content density (CCD) of rice wheat in extrapolation different growth period. The study confirmed the different growth period. The study confirmed the optimum predictive ability was good enough to develop a monitoring vegetation indices indices for for estimating estimating LAI LAI and and CCD CCD of of rice rice and and instrument predictive ability ability was good enough to develop develop monitoring of tomato content (Ding al., 2016). vegetation hadwas thechlorophyll best enough accuracy and reliability. The predictive good to aaetmonitoring wheat. Linear regression models were built for different growth period. The study confirmed the optimum vegetation indices for LAI and CCD ofestimating rice predictive was good enough to develop vegetation indices for estimating estimating CCD rice and and extrapolation instrument ability of tomato tomato chlorophyll content (Ding aet etmonitoring al., 2016). 2016). wheat. Linear Linear regression models LAI wereand built for ofestimating estimating instrument of chlorophyll content (Ding al., wheat. regression models were built for predictive ability was good enough to develop a monitoring instrument of tomato chlorophyll content (Ding et al., 2016). vegetation indices for estimating LAI and CCD of rice and wheat. Linear regression models were built for estimating instrument of tomato chlorophyll content (Ding et al., 2016). wheat. Linear regression models were built for estimating instrument of tomato chlorophyll content (Ding et al., 2016). 2405-8963 © IFAC (International Control) by Elsevier Ltd. All rights reserved. wheat. Linear models Federation were builtof Automatic for estimating Copyright © 2018, 2018regression IFAC 603Hosting Copyright 2018 responsibility IFAC 603Control. Peer review© of International Federation of Automatic Copyright ©under 2018 IFAC IFAC 603 Copyright © 603 Copyright © 2018 2018 IFAC 603 10.1016/j.ifacol.2018.08.131 Copyright © 2018 IFAC 603

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The study of Chang-Hua characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed and wheat crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with leaf chlorophyll content (LCC). This finding allowed the construction of a new red edge parameter, defined as red edge symmetry (RES). Compared to the commonly used red edge parameters, RES was a better predictor of LCC (Chang-Hua et al., 2010). The movement of red edge position to the direction of long wave or short wave is often used to estimate the chlorophyll content of leaves, and is an indicator of vegetation stress and senescence (Zhang et al., 2013, Peng et al., 2015).

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contains the image information and the spectral information, was obtained. Before the test starts, the system would be preheated to eliminate the effect of baseline drift. Then the hyperspectral imager was focused, and the system exposure time was 15 ms, and the moving speed of the mobile platform was 2.8 mm/s. The potato leaves were placed on the mobile platform, and the hyperspectral image data were collected by SpecVIEW software.

Potato is the fourth staple food in China, and the chlorophyll content of potato leaves is an important indicator of its pros and cons. The chlorophyll content detection methods currently used are mostly chemical analysis methods, which are time consuming and laborious and are destructive to the samples. And it is not conducive to the cultivation of excellent varieties. Hyperspectral imaging technology is a novel method to detect chlorophyll content in potato leaves. Research in this area is rarely reported, so its corresponding mapping software is also hard to find (Luo et al., 2014, Zhao et al., 2014).

1. Charge coupled device 2. Grating spectrometer 3. Imaging lens 4. Light source 5. One-dimensional electric station 6. Sample

Fig. 1. Schematic diagram of hyperspectral imaging system 2.3 Black and White Correction In order to eliminate the unevenness of light and the influence of the external environment, the black and white correction of hyperspectral images should be carried out before the data processing. Concrete steps as follows: in the data collection in the same environment, scanning calibration standard white plate to get all white image W, cover the lens cover and close the light collected all black image B. The original image is then corrected by formula (1).

In this study, after the hyperspectral data of the potato leaves were collected and pre-processed, the red edge position with the best correlation with the SPAD value of the potato leaves was calculated, and the diagnostic model of chlorophyll content was established. Combined with this diagnostic model, a mapping software that can quickly detect the chlorophyll distribution of potato leaves was designed and developed.

I=

2. MATERIALS AND METHODS

I0- B W- B

(1)

In the formula (1), I is black and white corrected image data, I0 is the original image data.

2.1 Experimental Object

2.4 Determination of Chlorophyll Content

The test subject is a potato variety called Atlantic. During April 2017, the potato plants were planted without special treatment in the experimental greenhouse of China Agricultural University. In May 23, 2017, the leaves of potato plants were removed as test samples when they grew at the flowering stage. 65 blade samples were collected randomly and packed into a sealed bag to keep the leaves fresh. It was brought back to the laboratory to collect the hyperspectral images of potato leaves.

The chemical analysis method needs to destroy the whole blade, and at the same time the weight of the test sample is required. In order to analyze the distribution of chlorophyll content in leaves, first of all, the area of the tested leaf samples was divided. Then using the handheld SPAD-502 chlorophyll meter for non-destructive testing of the division area, the SPAD value is used as the reference index for the content of chlorophyll.

2.2 Hyperspectral Image Acquisition

When measuring, the SPAD value was measured 3 times on the same area with the 10×10 pixel rectangular area as the Region of interesting (ROI), and the average value was taken as the chlorophyll value of the region. In order to ensure the consistency of the data, the acquisition of the hyperspectral data of the potato leaves and the acquisition of the SPAD value of the leaves were performed simultaneously. The complete process of collecting a leaf taked about two minutes. Finally, by measuring 65 potato leaf samples, 400 SPAD values of 400 regions were obtained. The region numbering

In this paper, the Gaia hyperspectral imaging system was used. Its structure is shown in Fig. 1. It is mainly composed of lens (OL23), area array CCD detector (LT365), spectrometer (V10E), uniform light source (2 bromine tungsten lamp), electronic control mobile platform, computer and control software. The resolution of the camera is 1394×1024 pixel, the spectral range is 382~1019 nm, the spectral resolution is 2.8 nm, and the sampling interval is 0.65 nm. Finally, a three-dimensional data cube, which 604

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mark of the SPAD value was measured so as to extract the corresponding spectrum.

a. Region of interesting in potato leaves

2.5 Calculation of Red Edge Position The peaks exist in different bands due to the bimodal phenomenon of the first derivative spectra, when the chlorophyll content changes. This leads to discontinuities in the relationship between the red edge and chlorophyll. The linear extrapolation method proposed by Cho can effectively reduce the discontinuity, so the linear extrapolation method is used to screen the red edge position in this study. The concrete steps are calculated in accordance with formula (2).

R= -

c1 - c2 m1 - m2

b. Original average spectral curves of 6 regions

(2)

Fig. 2. ROI selection and its spectral curve

In the formula (2), R represents the calculated red edge located wavelength (nm). c1 and m1 are the intercept and slope of the far infrared ray calculated by the first derivative spectrum. In addition, c2 and m2 are the intercept and slope of the near infrared ray calculated by the first derivative spectrum. And, the far infrared ray is calculated with two wavelengths of 679 nm and 694 nm, while the near infrared ray is calculated with two bands of 732 nm and 760 nm.

The correlation analysis was analyzed between the SPAD value of potato leaves and the average spectral reflectance of the corresponding regions. The results of the treatment were shown in Fig. 3. It is known from the diagram that the correlation coefficient values of 450~600 nm are significant, and the wavelengthes are located on the strong absorption band of chlorophyll. There is also a significant correlationship within 660~770 nm, and the wavelength range is the red edge area. This conclusion verified the feasibility of using red edge location to predict chlorophyll content in potato leaves.

3. RESULTS AND ANALYSIS 3.1 Hyperspectral Data Analysis The average spectrum of ROI in each sample was extracted by ENVI 5.1 and 10×10 unit pixel rectangular ROI region was selected for the collected potato leaves. As shown in Fig. 2a, the distribution of 6 regions of interest on a selected piece of leaf is shown. And the corresponding 6 original mean spectral curves are shown in Fig. 2b. The waveforms of the six spectral curves in Fig. 2b are relatively close to each other and in general fit the spectral characteristics of green plants. As a result of the strong reflection of chlorophyll, the wave peak appeared near the wavelength of 550 nm in the spectral curve. The wave valley appeared near the wavelength of 650~700 nm in the spectral curve due to the strong absorption of chlorophyll. Besides, the spectral reflectance of the near infrared region (700~750 nm) rose sharply. The hyperspectral images of potato leaves were collected in a closed lamp box, thus avoiding the influence of ambient light and other environmental factors. At the same time, the average spectrum corresponding to all the pixels in the ROI region was calculated, which had a smooth effect on the spectral curve. Therefore, the smooth spectrum could be used for subsequent data analysis.

Fig. 3. Results of correlation coefficient analysis The linear extrapolation method was used to calculate the statistical parameters of the red edge position of the hyperspectral reflectance of 400 samples, respectively. The maximum value is 769.47 nm, the minimum value is 660.54 nm, the average value is 703.64 nm, the standard deviation is 13, the deviation is 0.35, and the 95% confidence degree is 2.59. Thus, the distribution of red edge positions calculated by linear extrapolation method in 400 potato leaf samples is shown in Fig. 4. As shown in the Fig. 4, the median red edge position of different potato leaf SPAD values is between 702 nm and 706 nm approximately, which is consistent with previous research results. Meanwhile, the ratio distribution of the red edge position calculated by the linear extrapolation method for different potato leaf SPAD values can also be seen in Fig. 4.

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mixed programming of both C# and OPENCV. The system developed by C# had a series of advantages such as friendly interface, high code efficiency and fast execution, but there was a shortage of image processing and mapping display. However, the powerful image processing tools of OPENCV could improve the high spectrum image processing and data analysis. The design of software system used C# to invoke the library which was packaged by OPENCV. It could directly process and analyze complex hyperspectral image data, and effectively solve two problems. The system was designed with the structure, as shown in the Fig. 6. According to the software function requirements, there were 4 modules in the main program including image acquisition, image extraction, image processing and calculation of chlorophyll red edge estimation model and mapping display. And each function module could perform their respective tasks separately. The specific steps were as follows: after image acquisition and extraction, owing to the diagnosis model of chlorophyll content of potato leaves based on red edge location parameters, hyperspectral image processing and mapping display of potato leaves could be carried out. Then, the chlorophyll content distribution map of the potato leaf was plotted and shown in Fig. 7. The different colors and shades represented the chlorophyll content of potato leaves at different concentrations. The distribution of chlorophyll on both sides of the veins was relatively uniform, and the chlorophyll content of the veins was slightly higher than the chlorophyll content of the leaf sections. Among them, the veins were mainly green in the map, and the SPAD values ranged from about 40 to 50. The mesophylls were mainly blue in the distribution map, and the SPAD values ranged from about 30 to 40. Meanwhile, the visual expression of chlorophyll content of the leaf was realized.

Fig. 4. Red edge position at different SPAD ranges extracted by the linear extrapolation method In order to more accurately estimate the chlorophyll content of potato leaves, a linear fitting equation using the red edge position calculated by the linear extrapolation method and SPAD values of the leaves was established. Then the leaf chlorophyll content is retrieved by using the parameters of the red edge position, and the inversion model is shown in Fig. 5. The following is its linear fitting equation with the determinant coefficient of 0.8682.

y  1.0057 x  664.46

(3)

In the formula (3), x refers to the calculated red edge position, and y represents the SPAD value of the potato leaves.

Chlorophyll acquisition system based on red edge

Image acquisition

Fig. 5. Straight-line model fitting for the regression between leaf SPAD value and red edge position

Image extraction

Image processing and calculation of chlorophyll red edge estimation model

Fig. 6. Model of software structure

3.2 System Application Based on the above conclusions, a chlorophyll acquisition system based on red edge location was developed, which could more easily draw the visual distribution map of chlorophyll content of potato leaves. Finally, the chlorophyll content distribution of the potato leaves could be visualized, so as to provide support for subsequent estimation of chlorophyll content in potato field.

Fig. 7. Chlorophyll distribution of the potato leaf 4. CONCLUSION

The system was developed on the windows 7 operation system, using Visual Studio 2015 language. And the system's detection and analysis functions were realized through the 606

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In the study, the hyperspectral data of potato leaves were collected, and the average spectrum of the location of the SPAD value of potato leaves was extracted. In addition, the linear extrapolation method was used to screen the red edge of the spectral curve of the blade. Thus, the selected optimal red edge position was used to invert the chlorophyll content of potato leaves, and its estimating model was established. The decision coefficient of the model was 0.8682. It showed that the red edge location could be used to estimate the chlorophyll content of the leaves, and then monitored the crop growth status and guided the fertilization. In follow-up studies, a chlorophyll content acquisition system based on red spot position was designed and developed. It could be used to draw the distribution map of chlorophyll content in potato leaves. Besides, it could be used to analyze the difference of chlorophyll distribution and the dynamic characteristics of the growth period of potato leaves.

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The estimation model of chlorophyll content based on red edge location was a single factor linear relation model, which had the advantages of convenient solution and high computation accuracy. However, the work of this paper is based on blades, while hyperspectral remote sensing is based on crop canopy. In the follow-up study, the relationship between the spectral data of crop canopy and the content of chlorophyll needs to be further explored. 5. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation funded project (31501219) and the Chinese High Technology Research and Development Research Fund (2016YFD0300600-2016YFD0300606 and 2016YFD0300600-2016YFD0300610) and the Key Laboratory Project (BKBD-2017KF03, KF2018W003, 2018TC020) and foreign experts Project (MS2017ZGNY004). REFERENCES Chang-Hua, J. U., Tian, Y. C., Yao, X., Cao, W. X., Zhu, Y., & Hannaway, D. (2010). Estimating leaf chlorophyll content using red edge parameters. PEDOSPHERE, 20(5), 633-644. Ding, Y., Zhang, J., Li, X., & Li, M. (2016). Estimation of chlorophyll content of tomato leaf using spectrum red edge position extraction algorithm. Transactions of the Chinese Society for Agricultural Machinery. Dordas, C. (2017). Nitrogen nutrition index and leaf chlorophyll concentration and its relationship with nitrogen use efficiency in barley. Journal of Plant Nutrition, 40(8), 1190-1203. Feng, H. K., Yang, F. Q., Yang, G. J., Li, Z. H., Pei, H. J., & Xing, H. M. (2018). Estimation of chlorophyll content in apple leaves base on spectral feature parameters. Transactions of the Chinese Society of Agricultural Engineering. Feng, Y., Fan, Y. M., Li, J. L., Qian, Y. R., Yan, W., & Jie, Z. (2010). Estimating lai and ccd of rice and wheat using hyperspectral remote sensing data. Transactions of the

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