Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing

Authors

  • Liu Xuefeng 1. Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China; 2. Institute of Optics-Mechanics-Electronics Technology and Application, East China Jiaotong University, Nanchang 330013, China
  • Lyu Qiang 1. Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China; 2. Institute of Optics-Mechanics-Electronics Technology and Application, East China Jiaotong University, Nanchang 330013, China
  • He Shaolan 1.Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China
  • Yi Shilai 1.Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China;
  • Hu Deyu 3.College of Horticulture and landscape, Southwest University, Chongqing 400715, China
  • Wang Zhitao 1.Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China;
  • Xie Rangjin 1.Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China;
  • Zheng Yongqiang 1. Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China;
  • Deng Lie 1. Citrus Research Institute, Southwest University-Chinese Academy of Agricultural Sciences, Chongqing 400712, China; 2. Institute of Optics-Mechanics-Electronics Technology and Application, East China Jiaotong University, Nanchang 330013, China

DOI:

https://doi.org/10.25165/ijabe.v9i5.2246

Keywords:

citrus canopy, low-altitude remote sensing, carbon and nitrogen contents, soluble sugar, starch, estimation

Abstract

Abstract: The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy. The multispectral imagery of Tarocco blood orange (Citrus sinensis L. Osbeck) plant canopy was obtained by a multispectral camera array mounted at an eight-rotor unmanned aerial vehicle (UAV) flying at an altitude of 100 m above the canopy in Wanzhou District of Chongqing Municipality, China. Average spectral reflectance data of the whole canopy, mature leaf areas and young leaves areas were extracted from the imagery. Two spectral pre-processing methods, multiplicative scatter correction (MSC) and standard normal variable (SNV), and two modeling methods, the partial least squares (PLS) and the least squares support vector machine (LS-SVM), were adopted and compared for their prediction accuracy of total content of nitrogen, soluble sugar and starch in the leaves. The results showed that, based on the spectral data extracted from the mature leaves in the multispectral imagery, the PLS model based on the original spectrum obtained a Rp (correlation coefficient) of 0.6469 and RMSEP (root mean squares error of prediction ) of 0.1296, suggested that it was the best for the prediction of total nitrogen content; the PLS model based on MSC (multiplicative scatter correction) spectrum pre-processing was the best for predicting total soluble sugar content (Rp=0.6398 and RMSEP=8.8891); and the LS-SVM model based on MSC was the best for the starch content prediction (Rp=0.6822 and RMSEP=14.9303). The prediction accuracy for carbon and nitrogen contents based on the spectral data extracted from the whole canopy and the young leaves were lower than that from the mature leaves. The results indicate that it is feasible to estimate the carbon and nitrogen contents by low-altitude airborne multispectral images. Keywords: citrus canopy, low-altitude remote sensing, carbon and nitrogen contents, soluble sugar, starch, estimation DOI: 10.3965/j.ijabe.20160905.2246 Citation: Liu X F, Lyu Q, He S L, Yi S L, Hu D Y, Wang Z T, et al. Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing. Int J Agric & Biol Eng, 2016; 9(5): 149－157.

References

Liu C G, Wang Y J, Pan K W, Jin Y Q, Jin L, Li W, et al. Photosynthetic carbon and nitrogen metabolism and the relationship between their metabolites and lipid peroxidation in dwarf bamboo (Fargesia rufa Yi) during drought and subsequent recovery. Trees, 2015; 29(6): 1–15.

Ren C, Liu J, Gong Q. Functions of autophagy in plant carbon and nitrogen metabolism. Frontiers in Plant Science, 2014; 5: 301.

Commichau F M, Forchhammer K, Stülke J. Regulatory links between carbon and nitrogen metabolism. Current Opinion in Microbiology, 2006; 9(2): 167–172.

Sun W F, Huang A, Sang Y Y, Fu Y, Yang Z B. Carbon-Nitrogen interaction modulates plant growth and expression of metabolic genes in rice. Journal of Plant Growth Regulation, 2013; 32(3): 575–584.

Lemaire G. Diagnosis of the nitrogen status in crops. Springer Science & Business Media, 2012.

Fontes P C R. Dianostics of the nutritional state of plants. Viçosa, Editora UFV, Universidade Federal de Viçosa, 2004.

Xiang H, Tian L. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering, 2011; 108(2): 174–190.

Zhang C, Kovacs J M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 2012; 13(6): 693–712.

Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 2013; 114(4): 358–371.

Yang C, Everitt J H, Murden D. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 2011; 75(2): 347–354.

Mewes T, Franke J, Menz G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agriculture, 2011; 12(6): 795–812.

Kumar A, Lee W S, Ehsani R J, Albrigo L. G, Yang C H, Mangan R L. Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing, 2012; 6(1): 241–243.

Ines A V M, Das N N, Hansen J W, Njoku E G. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment, 2013; 138: 149–164.

Rembold F, Atzberger C, Savin I, Rojas O. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sensing, 2013; 5(4): 1704–1733.

Li B, Liu R Y, Liu S H, Liu Q, Liu F, Zhou G Q. Monitoring vegetation coverage variation of winter wheat by low altitude UAV remote sensing system. Transactions of the CSAE, 2012; 28(13): 160–165. (in Chinese with English abstract)

Li F, Victor A, Zhao H, Zhao Y J, Cui X F. PLSR-based airborne hyperspectral remote sensing retrieval of leaf nitrogen content in potato fields. Chinese Journal of Agrometeorology, 2014; 35(3): 338–343. (in Chinese with English abstract)

Miller J R, Prasher S O, Goel P K, Landry J A, Patel R M, Viau A A. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Transactions of the ASAE, 2003; 46(4): 1235–1246.

Everitt J H, Bradford J M. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Transactions of the ASAE, 2004; 47(3): 915–924.

Rodriguez J C, Duchemin B, Hadria R, Watts C, Garatula J, Chehbouni A, et al. Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico. Agronomie, 2004; 24(6-7): 295–304.

Uno Y, Prasher S O, Lacroix R, Goel P K, Karimi Y, Viau A, et al. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Computers & Electronics in Agriculture, 2005; 47(2): 149–161.

Ye X, Sakai K. Application of airborne hyperspectral imagery to estimating fruit yield in citrus. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011 3rd Workshop on. IEEE, 2011: 1–6.

Wang X K. Experimental principle and technology of plant physiology and biochemistry (Second Edition), Beijing: Higher Education Press, 2006; 202–206. (in Chinese)

Bao S D. Soil and agricultural chemistry analysis (Third Edition), Beijing: China Agriculture Press, 2000; 303–323. (in Chinese)

Windig W, Shaver J, Bro R. Loopy MSC: a simple way to improve multiplicative scatter correction. Applied Spectroscopy, 2008; 62(10): 1153–1159.

Barnes R J, Dhanoa M S, Lister S J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 1989; 43(5): 772–777.

Rosipal R, Krämer N. Overview and recent advances in partial least squares//Subspace, latent structure and feature selection. Springer Berlin Heidelberg, 2006; 34–51.

Suykens J. Least squares support vector machines for classification and nonlinear modelling. Neural Network World, 2000; 10(1-2): 29–48.

Knipling E B. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1970; 1: 155–159

Chen S Y, Ouyang Y C, Chang C I. Weighted radial basis function kernels-based support vector machines for multispectral image classification. IEEE, 2012; 4339–4342.

Yasin Z M, Rahman T K A, Zakaria Z. Optimal least squares support vector machines parameter selection in predicting the output of distributed generation. IEEE, 2014; 152–157.

He T F. Citrus science. Beijing: China Agriculture Press. 1999. pp: 73–76. (in Chinese)

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; 46(016): 3504–3513. (in Chinese with English abstract)

Li J M, Ye X J, Wang Q N, Zhang C, He Y. Development of prediction models for determining N content in citrus leaves based on hyperspectral imaging technology. Spectroscopy and Spectral Analysis, 2014; 34(1): 212–216. (in Chinese with English abstract)

Zhang Y, Zheng L H, Li M Z, Deng X L, Wang S C, Zhang F, et al. Construction of apple tree leaves nutrients prediction model based on spectral analysis. Transactions of the CSAE, 2013; 29(8): 171–178. (in Chinese with English abstract)

Downloads

Published

2016-09-30

How to Cite

Xuefeng, L., Qiang, L., Shaolan, H., Shilai, Y., Deyu, H., Zhitao, W., … Lie, D. (2016). Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing. International Journal of Agricultural and Biological Engineering, 9(5), 149–157. https://doi.org/10.25165/ijabe.v9i5.2246

Issue

Vol. 9 No. 5 (2016): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).