Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors
Keywords:
citrus, remote sensing, bio-sensor, chlorophyll detection, spectrum, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), spatial distribution mapAbstract
The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards. The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed. Additionally, the associations of the leaf SPAD (soil and plant analyzer development) value with the ratio vegetation index (RVI) and normalized differential vegetation index (NDVI) were analyzed. The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method. Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created. The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor, FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant. The measures of goodness of fit of the predictive models were R2=0.7063, RMSECV=3.7892, RE=5.96%, and RMSEP=3.7760 based on RVI(570/800) and R2=0.7343, RMSECV=3.6535, RE=5.49%, and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)]. The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard, which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management. Keywords: citrus, remote sensing, bio-sensor, chlorophyll detection, spectrum, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), spatial distribution map DOI: 10.25165/j.ijabe.20181102.3189 Citation: Wang K J, Li W T, Deng L, Lyu Q, Zheng Y Q, Yi S L, et al. Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors. Int J Agric & Biol Eng, 2018; 11(2): 164–169.References
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[25] Zhang X, Yuan H F, Guo Z, Song C F, Li X Y, Xie J C. Study of the over-fitting in building PLS model using orthogonal signal correction. Spectroscopy and Spectral Analysis, 2011; 6: 1688–1691. (in Chinese)
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[28] Tian X, He S L, Lyu Q, Yi S L, Xie R J, Zheng Y Q, et al. Determination of photosynthetic pigments in citrus leaves based on hyperspectral images data. Spectroscopy and Spectral Analysis, 2014; 9: 45. (in Chinese)
[29] Liu X F, Lyu Q, He S L, Yi S L, Xie R J, Zheng Y Q, et al. Estimation of nitrogen and pigments content in citrus canopy by low-altitude remote sensing. Journal of Remote Sensing, 2015; 19(6): 1007–1018. (in Chinese)
[2] Nijs I, Behaeghe T, Impens I. Leaf nitrogen content as a predictor of photosynthetic capacity in ambient and global change conditions. Journal of Biogeography, 1995; 177–183.
[3] Wood C W, Reeves D W, Himelrick D G. Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: A review. Proc. Agron. Soc. NZ, 1993; 23: 1–9.
[4] Guan J Y, Hao Z B, Zhang D, Wang X L. Detection and biological function of chlorophyll. Journal of Northeast Agricultural University, 2009; 12: 130–134. (in Chinese)
[5] Cerovic Z G, Masdoumier G, Ghozlen N B, Latouche G. A new optical leaf clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiologia Plantarum, 2012; 146(3): 251–260.
[6] Netto A T, Campostrini E, de Oliveira J G, Bressan-Smith R E. Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Scientia Horticulturae, 2005; 104(2): 199–209.
[7] Hoel B O, Solhaug K A. Effect of irradiance on chlorophyll estimation with the Minolta SPAD-502 leaf chlorophyll meter. Annals of Botany, 1998; 82(3): 389–392.
[8] Markwell J, Osterman J C, Mitchell J L. Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research, 1995; 46(3): 467–472.
[9] Li G H, Xue L H, You J, Wang S H. Spatial distribution of leaf N content and SPAD value and determination of the suitable leaf for N diagnosis in rice. Scientia Agricultura Sinica, 2007; 40(6): 1127–1134.
[10] Asai H, Samson B K, Stephan H M, Songyikhangsuthor K, Homma K, Kiyono Y, et al. Biochar amendment techniques for upland rice production in Northern Laos: 1. Soil physical properties, leaf SPAD and grain yield. Field Crops Research, 2009; 111(1): 81–84.
[11] Pinar A, Curran P J. Technical note grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing, 1996; 17(2): 351–357.
[12] Gitelson A A, Merzlyak M N, Lichtenthaler H K. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 1996; 148(3): 501–508.
[13] Wang X, Ji H Y. Quantitative analysis of chlorophyll in wheat leaf based on reflection spectroscopy and transmission spectroscopy using portable spectrometer. Chinese Agricultural Science Bulletin, 2011; 21: 39–43. (in Chinese)
[14] Zhang H, Yao X G, Zhang X B, Zhu L L, Ye S T, Zheng K F, et al. Measurement of rice leaf chlorophyll and seed nitrogen contents by using Multi-Spectral imagine. Chinese Journal of Rice Science, 2008; 5: 555–558. (in Chinese)
[15] Deng X L, Li M Z, Zheng L H, Zhang Y, Sun H. Estimating chlorophyll content of apple leaves based on preprocessing of reflectance spectra. Transactions of the CSAE, 2014; 14: 140–147. (in Chinese)
[16] Yue X J, Quan D P, Hong T S, Wang J, Qu X M, Gan H M. Non-destructive hyperspectral measurement model of chlorophyll content for citrus leaves. Transactions of the CSAE, 2015; 1: 294–302. (in Chinese)
[17] Song X Y, Wang J H, Yang G J, Gui B, Chang H. Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters. Spectroscopy and Spectral Analysis, 2014; 7: 044. (in Chinese)
[18] Wang Y J. Analysis of correlation coefficient and coefficient of determination. Academic Annual Conference of Science and Technology Journals in the Yangtze River Basin and the Northwest Region of China, 2008.
[19] Fang X Y, Zhu X C, Wang L, Zhao G X. Hyperspectral monitoring of the canopy chlorophyll content at apple tree prosperous fruit stage. Scientia Agricultura Sinica, 2013; 16: 3504–3513.
[20] Geladi P, Kowalski B R. Partial least-squares regression: A tutorial. Analytica Chimica Acta, 1986; 185: 1–17.
[21] Wang Z P, Zhou G H, Luo G G. Partial least square method (PLS) and its application in analytical chemistry. Analytical Chemistry, 1989; 17(7): 662–669.
[22] Wang Q, Yi Q X, Bao A M, Luo Y, Zhao J. Estimating chlorophyll density of cotton canopy by hyperspectral reflectance. Transactions of the CSAE, 2012; 15:125–132. (in Chinese)
[23] Galvao R K H, Araujo M C U, Jose G E, Pontes M J C, Silva E C, Saldanha T C B. A method for calibration and validation subset partitioning. Talanta, 2005; 67(4): 736–740.
[24] Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynthesis Research, 2007; 91(1): 37–46.
[25] Zhang X, Yuan H F, Guo Z, Song C F, Li X Y, Xie J C. Study of the over-fitting in building PLS model using orthogonal signal correction. Spectroscopy and Spectral Analysis, 2011; 6: 1688–1691. (in Chinese)
[26] Wichern D W. Applied multivariate statistical analysis. Tsinghua University Press Co., Ltd., 2001.
[27] Huang J F, Blackburn G A. Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data. International Journal of Remote Sensing, 2011; 32(24): 9375–9396.
[28] Tian X, He S L, Lyu Q, Yi S L, Xie R J, Zheng Y Q, et al. Determination of photosynthetic pigments in citrus leaves based on hyperspectral images data. Spectroscopy and Spectral Analysis, 2014; 9: 45. (in Chinese)
[29] Liu X F, Lyu Q, He S L, Yi S L, Xie R J, Zheng Y Q, et al. Estimation of nitrogen and pigments content in citrus canopy by low-altitude remote sensing. Journal of Remote Sensing, 2015; 19(6): 1007–1018. (in Chinese)
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Published
2018-03-31
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Wang, K., Li, W., Deng, L., Lyu, Q., Zheng, Y., Yi, S., … He, S. (2018). Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors. International Journal of Agricultural and Biological Engineering, 11(2), 164–169. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3189
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Information Technology, Sensors and Control Systems
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