Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion
Keywords:
polarized spectra, hyperspectral, soluble sugar (SS), total nitrogen (N), data fusion, tomato leafAbstract
Polarized spectra–hyperspectral data fusion technique was used to estimate the soluble sugar (SS), total nitrogen (N), and their ratio (SS/N), of greenhouse tomato leaves. Fresh tomato leaves of five different growth stages (seedling, flowering, initial fruiting, mid-fruiting and picking stage) and five different nitrogen treatments (severe stress 25%, moderate stress 50%, mild stress 75%, normal 100%, and excess 150%) at every stage were collected for spectra acquisition and SS and N determination. Polarized reflectance spectra were acquired with a polarization reflectance spectrum spectro-goniophotometer system and four polarization degree features were extracted. Hyperspectral data were collected with a hyperspectral imaging system and four reflectance spectrum features and eight image features were extracted. Initially, models were built with polarization degree features, image features, and spectral features respectively. Linear and nonlinear fusion methods were comparatively used for modeling based on normalized data of the three sources. The results suggest that the performances of SS/N models are better than those of N and SS models, and the prediction capability of the Support Vector Machine (SVM) models of N and SS/N are superior to those obtained with single kind feature. This work indicates that the polarized spectrum-hyperspectral multidimensional information detecting method can feasibly judge the tomato nutrient stress conditions. Multi-features data fusion analysis technique can enhance the prediction accuracy of spectral diagnostics technology in precision agriculture. Keywords: polarized spectra, hyperspectral, soluble sugar (SS), total nitrogen (N), data fusion, tomato leaf DOI: 10.25165/j.ijabe.20201302.4280 Citation: Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197.References
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[28] Raven P N, Jordan D L, Smith C E. Polarized directional reflectance from laurel and mullein leaves. Optical Engineering, 2002; 41(5): 1002–1012.
[29] Shibayama M, Sakamoto T, Kimura A. A multiband polarimetric imager for field crop survey:―Instrumentation and preliminary observations of heading-stage wheat canopies―. Plant Production Science, 2011; 14(1): 64–74.
[30] del Ŕıo L F, Arwin H, Järrendahl K. Polarization of light reflected from Chrysina gloriosa under various illuminations. Materials Today: Proceedings, 2014; 1: 172–176.
[31] Maxwell D J, Partridge J C, Roberts N W, Boonham N, Foster G D. The effects of surface structure mutations in Arabidopsis thaliana on the polarization of reflections from virus-infected leaves. PloS One, 2017; 12(3): e0174014.
[32] Pourreza A, Lee W S, Etxeberria E, Zhang Y. Identification of citrus Huanglongbing disease at the pre-symptomatic stage using polarized imaging technique. IFAC-PapersOnLine, 2016; 49(16): 110–115.
[33] Wu T X, Zhang L F, Cen Y, Huang C P, Sun X J, Zhao H Q, et al. Polarized spectral measurement and analysis of sedum spectabile boreau using a field imaging spectrometer system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013; 6(2): 724–730.
[34] Tan S X, Kabir Khan A S M. Water stress detection of lilac leaves using a polarized laser. In: Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII. San Diego: SPIE, 2015. 96100M.
[35] Song K S, Zhang B, Zhao Y S, Wang Z M, Du J. Study of polarized reflectance of corn leaf and its relationship to laboratory measurements of bi-directional reflectance. Journal of Remote Sensing, 2007; (5): 632–640.
[36] Liao Q H, Zhao C J, Yang G J, Coburn C, Wang J H, Zhang D Y, et al. Estimation of leaf area index by using multi-angular hyperspectral imaging data based on the two-layer canopy reflectance model. Intelligent Automation & Soft Computing, 2013; 19(3): 295–304.
[37] Lü Y F. Study of hyperspectral polarized reflectance of vegetation canopy at nadir viewing direction. Spectroscopy and Spectral Analysis, 2013; 33(4): 1028–1031.
[38] Jay S, Maupas F, Bendoula R, Gorretta N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 2017; 210: 33–46.
[39] Mao H P, Zhu W J, Liu H Y. Determination of nitrogen and potassium content in greenhouse tomato leaves using a new spectro-goniophotometer. Crop and Pasture Science, 2014; 65(9): 888–898.
[40] Zhu W J, Mao H P, Li Q L, Liu H Y, Sun J, Zuo.Z Y, et al. Study on the polarized reflectance-hyperspectral information fusion technology of tomato leaves nutrient diagnoses. Spectroscopy and Spectral Analysis, 2014; 34(9): 2500–2505.
[41] Chen Y W, Zeng Q Y, Pan Y X, Zhao Y. A new method of military false target identification. Electronic Design Engineering, 2011; 19(16): 89–92. (in Chinese)
[42] Story D, Kacira M, Kubota C, Akoglu A, An L L. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and electronics in agriculture, 2010; 74(2): 238–243.
[43] Vittayapadung S, Zhao J W, Quansheng C, and Chuaviroj R. Application of FT-NIR spectroscopy to the measurement of fruit firmness of" Fuji" apples. Maejo International Journal of Science and Technology, 2008; 2(1): 13–23.
[44] Ouyang Q, Zhao J W, Chen Q S. Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm. Spectrochim Acta A: Mol Biomol Spectrosc, 2015; 151: 280–285.
[45] Norgaard L, Saudland A, Wagner J, Nielsen J P, Munck L, Engelsen S B. Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy, 2000; 54(3): 413–419.
[46] Tewari J C, Dixit V, Cho B K, Malik K A. Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2008; 71(3): 1119–1127.
[47] Ouyang Q, Zhao J W, Chen Q S. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. Anal Chim Acta, 2014; 841: 68–76.
[48] Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995; 20: 273–297.
[49] Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems, 1989; 2: 303–314.
[50] Wang L, Cui N, Fan Y H, Miao Q, Qu B,,Li T L. Comparison of carbohydrate metabolism after anthesis in the leaves of two tomato types. Journal of Shenyang Agriculture University, 2012; 43(4): 482–485. (in Chinese)
[51] Li Y X, Li J H, He L L, Gong G Y, Li T L. The effect of N. P. K mixed application on yields and quality of tomato in solar greenhouse. China Vegetables, 1997; 4: 12–15.
[52] Story D, Kacira M, Kubota C, Akoglu A. Morphological and textural plant feature detection using machine vision for intelligent plant health, growth and quality monitoring. Acta Hortic, 2011; 893: 299–306.
[53] Vanderbilt V C, Grant L, Daughtry C S T. Polarization of light scattered by vegetation. Proceedings of the IEEE, 1985; 73(6): 1012–1024.
[54] Mao H P, Gao H Y, Zhang X D, Kumi F. Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision. Scientia Horticulturae, 2015; 184: 1–7.
[2] Schlüter U, Mascher M, Colmsee C, Scholz U, Bräutigam A, Fahnenstich H, et al. Maize source leaf adaptation to nitrogen deficiency affects not only nitrogen and carbon metabolism but also control of phosphate homeostasis. Plant Physiology, 2012; 160(3): 1384–1406.
[3] Jia F F, Liu G S, Liu D S, Zhang Y Y, Fan W G, Xing X X. Comparison
of different methods for estimating nitrogen concentration in flue-cured tobacco leaves based on hyperspectral reflectance. Field Crops Research, 2013; 150: 108–114.
[4] De Pascale S, Maggio A, Orsini F, Barbieri G. Cultivar, soil type, nitrogen source and irrigation regime as quality determinants of organically grown tomatoes. Scientia Horticulturae, 2016; 199: 88–94.
[5] Sritontip C, Khaosumain Y, Changjeraja S. Different nitrogen concentrations affecting chlorophyll and dry matter distribution in sand-cultured longan trees. Acta Hortic, 2014; 1024: 227–233.
[6] Oroka F O. Mulching effects and nitrogen application on the performance of Zea mays L: crop growth and nutrient accumulation. International Letters of Natural Sciences, 2016; 51: 36–42.
[7] Guzman J G, Godsey C B, Pierzynski G M, Whitney D A, Lamond R E. Effects of tillage and nitrogen management on soil chemical and physical properties after 23 years of continuous sorghum. Soil and Tillage Research, 2006; 91(1-2): 199–206.
[8] Horchani F, Hajri R, Aschi-Smiti S. Effect of ammonium or nitrate nutrition on photosynthesis, growth, and nitrogen assimilation in tomato plants. Journal of Plant Nutrition and Soil Science, 2010; 173(4): 610–617.
[9] Fatima T, Teasdale J R, Bunce J, Mattoo A K. Tomato response to legume cover crop and nitrogen: differing enhancement patterns of fruit yield, photosynthesis and gene expression. Functional Plant Biology, 2012; 39(3): 246–254.
[10] Kruse J, Hänsch R, Mendel R R, Rennenberg H. The role of root nitrate reduction in the systemic control of biomass partitioning between leaves and roots in accordance to the C/N-status of tobacco plants. Plant and Soil, 2010; 332: 387–403.
[11] Prause J Fernandez Lopez C. Litter decomposition and lignin/cellulose and lignin/total nitrogen rates of leaves in four species of the Argentine Subtropical forest. Agrochimica, 2007; 51(6): 294–300.
[12] Korus K, Conley M E, Paparozzi E T. Qualitative and quantitative analysis of soluble sugar content in leaves of hydroponically grown Swedish ivy at varying periods of nitrogen deficiency and subsequent re-greening. HortScience, 2008; 1285.
[13] Barickman T C, Kopsell D A, Sams C E. Abscisic acid improves tomato fruit quality by increasing soluble sugar concentrations. Journal of Plant Nutrition, 2017; 40(7): 964–973.
[14] Baxter C J, Carrari F, Bauke A, Overy S, Hill S A, Quick P W, et al. Fruit carbohydrate metabolism in an introgression line of tomato with increased fruit soluble solids. Plant and Cell Physiology, 2005; 46(3): 425–437.
[15] Taiz L Zeiger E, Møller I M, Murphy A. Plant physiology and development. Sinauer Associates, 2015.
[16] Zhao D L, Reddy K R, Kakani V G, Reddy V R. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy, 2005; 22(4): 391–403.
[17] Shi.J Y, Zou X B, Zhao J W, Wang K L, Chen Z W, Huang X W, et al. Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging. Scientia Horticulturae, 2012; 138: 190–197.
[18] Sun J, Wang Y, Wu X H, Zhang X D. The nitrogen quantitative model based on hyperspectral image of tomato leaf. Advanced Materials
Research, Trans, 2012; 466–467: 191–195.
[19] Liu Y L, Lyu Q, He S L, Yi S L, Liu X F, Xie R J, et al. Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Int J Agric & Biol Eng , 2015; 8(2): 80–88.
[20] Zhu Y, Wang W, Yao X. Estimating leaf nitrogen concentration (LNC) of cereal crops with hyperspectral data. In: Thenkabail P S (Ed.), Lyon J G (Ed.). Hyperspectral Remote Sensing of Vegetation. Boca Raton: CRC Press, 2012; pp.187–206.
[21] Sun J, Shi S, Gong W, Yang J, Du L, Song S L, et al. Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer. Sci Rep, 2017; 7: 40362.
[22] Clevers J G P W, Kooistra L. Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012; 5(2): 574–583.
[23] Song X, Xu D Y, He L, Feng W, Wang Y H, Wang Z J, et al. Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat. Precision Agriculture, 2016; 17: 721–736.
[24] Zhang C, Liu F, Kong W W, Cui C, He Y, Zhou W J. Estimation and visualization of soluble sugar content in oilseed rape leaves using hyperspectral imaging. Transaction of the ASABE, 2016; 59(6): 1499–1505.
[25] Xu X G, Gu X H, Song X Y, Xu B, Yu H Y, Yang G J, et al. Assessing the ratio of leaf carbon to nitrogen in winter wheat and spring barley based on hyperspectral data. In: Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII. Edinburgh: SPIE, 2016. 999810.
[26] Shi R H, Niu Z, Zhuang D F. Feasibility of estimating leaf C/N ratio with hyperspectral remote sensing data. Remote Sensing Technology and Application, 2003; 18(2): 76–80. (in Chinese)
[27] Chen H B, Fan X H, Han Z G. A review on remote sensing from POLDER multispectral, multidirectional and polarized measurements. Remote Sensing Technology and Application, 2006; 21(2): 83–92. (in Chinese)
[28] Raven P N, Jordan D L, Smith C E. Polarized directional reflectance from laurel and mullein leaves. Optical Engineering, 2002; 41(5): 1002–1012.
[29] Shibayama M, Sakamoto T, Kimura A. A multiband polarimetric imager for field crop survey:―Instrumentation and preliminary observations of heading-stage wheat canopies―. Plant Production Science, 2011; 14(1): 64–74.
[30] del Ŕıo L F, Arwin H, Järrendahl K. Polarization of light reflected from Chrysina gloriosa under various illuminations. Materials Today: Proceedings, 2014; 1: 172–176.
[31] Maxwell D J, Partridge J C, Roberts N W, Boonham N, Foster G D. The effects of surface structure mutations in Arabidopsis thaliana on the polarization of reflections from virus-infected leaves. PloS One, 2017; 12(3): e0174014.
[32] Pourreza A, Lee W S, Etxeberria E, Zhang Y. Identification of citrus Huanglongbing disease at the pre-symptomatic stage using polarized imaging technique. IFAC-PapersOnLine, 2016; 49(16): 110–115.
[33] Wu T X, Zhang L F, Cen Y, Huang C P, Sun X J, Zhao H Q, et al. Polarized spectral measurement and analysis of sedum spectabile boreau using a field imaging spectrometer system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013; 6(2): 724–730.
[34] Tan S X, Kabir Khan A S M. Water stress detection of lilac leaves using a polarized laser. In: Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII. San Diego: SPIE, 2015. 96100M.
[35] Song K S, Zhang B, Zhao Y S, Wang Z M, Du J. Study of polarized reflectance of corn leaf and its relationship to laboratory measurements of bi-directional reflectance. Journal of Remote Sensing, 2007; (5): 632–640.
[36] Liao Q H, Zhao C J, Yang G J, Coburn C, Wang J H, Zhang D Y, et al. Estimation of leaf area index by using multi-angular hyperspectral imaging data based on the two-layer canopy reflectance model. Intelligent Automation & Soft Computing, 2013; 19(3): 295–304.
[37] Lü Y F. Study of hyperspectral polarized reflectance of vegetation canopy at nadir viewing direction. Spectroscopy and Spectral Analysis, 2013; 33(4): 1028–1031.
[38] Jay S, Maupas F, Bendoula R, Gorretta N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 2017; 210: 33–46.
[39] Mao H P, Zhu W J, Liu H Y. Determination of nitrogen and potassium content in greenhouse tomato leaves using a new spectro-goniophotometer. Crop and Pasture Science, 2014; 65(9): 888–898.
[40] Zhu W J, Mao H P, Li Q L, Liu H Y, Sun J, Zuo.Z Y, et al. Study on the polarized reflectance-hyperspectral information fusion technology of tomato leaves nutrient diagnoses. Spectroscopy and Spectral Analysis, 2014; 34(9): 2500–2505.
[41] Chen Y W, Zeng Q Y, Pan Y X, Zhao Y. A new method of military false target identification. Electronic Design Engineering, 2011; 19(16): 89–92. (in Chinese)
[42] Story D, Kacira M, Kubota C, Akoglu A, An L L. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and electronics in agriculture, 2010; 74(2): 238–243.
[43] Vittayapadung S, Zhao J W, Quansheng C, and Chuaviroj R. Application of FT-NIR spectroscopy to the measurement of fruit firmness of" Fuji" apples. Maejo International Journal of Science and Technology, 2008; 2(1): 13–23.
[44] Ouyang Q, Zhao J W, Chen Q S. Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm. Spectrochim Acta A: Mol Biomol Spectrosc, 2015; 151: 280–285.
[45] Norgaard L, Saudland A, Wagner J, Nielsen J P, Munck L, Engelsen S B. Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy, 2000; 54(3): 413–419.
[46] Tewari J C, Dixit V, Cho B K, Malik K A. Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2008; 71(3): 1119–1127.
[47] Ouyang Q, Zhao J W, Chen Q S. Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. Anal Chim Acta, 2014; 841: 68–76.
[48] Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995; 20: 273–297.
[49] Cybenko G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Systems, 1989; 2: 303–314.
[50] Wang L, Cui N, Fan Y H, Miao Q, Qu B,,Li T L. Comparison of carbohydrate metabolism after anthesis in the leaves of two tomato types. Journal of Shenyang Agriculture University, 2012; 43(4): 482–485. (in Chinese)
[51] Li Y X, Li J H, He L L, Gong G Y, Li T L. The effect of N. P. K mixed application on yields and quality of tomato in solar greenhouse. China Vegetables, 1997; 4: 12–15.
[52] Story D, Kacira M, Kubota C, Akoglu A. Morphological and textural plant feature detection using machine vision for intelligent plant health, growth and quality monitoring. Acta Hortic, 2011; 893: 299–306.
[53] Vanderbilt V C, Grant L, Daughtry C S T. Polarization of light scattered by vegetation. Proceedings of the IEEE, 1985; 73(6): 1012–1024.
[54] Mao H P, Gao H Y, Zhang X D, Kumi F. Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision. Scientia Horticulturae, 2015; 184: 1–7.
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2020-04-10
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Zhu, W., Li, J., Li, L., Wang, A., Wei, X., & Mao, H. (2020). Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. International Journal of Agricultural and Biological Engineering, 13(2), 189–197. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4280
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