Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize

Authors
F. Kahriman 1
K. Demirel 2
M. Inalpulat 2
C. O. Egesel 3
L. Genc 2
1 Department of Field Crops, Faculty of Agriculture, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
2 Agricultural Sensor and Remote Sensing Laboratory, Faculty of Agriculture, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
3 Department of Agricultural Biotechnology, Faculty of Agriculture, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Abstract
The aim of this study was to develop and validate qualitative and quantitative models to discriminate different types of maize and also estimate biochemical constituents. Spectral data were taken from the central leaf of randomly-chosen plants grown in field trials in 2011 and 2012. Leaf chlorophyll and protein content and stalk protein content were determined in the same plants. Four different Support Vector Machine (SVM) models were generated and validated in this study. In qualitative models, maize type was designated as dependent variable while Full Spectral (FS) data (400-1,000 nm) and Spectral Indices (SI) data (34 indices/bands) were independent variables. In the two quantitative models (SVMR-FS and SVMR-SI), independent variables were the same, whereas dependent variables were assigned as the quantitatively measured traits. Results showed the qualitative models to be a robust method of classification for distinguishing different maize types, such as High Oil Maize (HOM), High Protein Maize (HPM) and standard (NORMAL) maize genotypes. The SVMC-FS model was superior to SVMC-SI in terms of the genotypic classification of maize plants. Quantitative models with full spectral data gave more robust prediction than the others. The best prediction result (RMSEC= 222.4 µg g-1, R2 for Cal= 0.739, SEP= 213.3 µg g-1; RPD= 2.04 and r= 0.877) was obtained from the SVMR-FS model developed for chlorophyll content. Indirect estimation models, based on relationships between leaf-based spectral measurements and leaf and stalk protein content, were less satisfactory.

Keywords

1. Abu-Kalaf, N. and Salman, M. 2014. Visible/Near Infrared (VIS/NIR) Spectroscopy and Multivariate Data Analysis (MVDA) for Identification and Quantification of Olive Leaf Spot (OLS) disease. Palest. Tech. Univ. Res. J., 2: 1-8.
2. Abu-Kalaf, M. 2015. Sensing Tomato’s Pathogen Using Visible/Near Infrared (VIS/NIR) Spectroscopy and Multivariate Data Analysis (MVDA). Palest. Tech. Univ. Res. J., 3: 12-22.
3. Babar, M. A., Reynolds, M. P., Van Ginkel, M., Klatt, A. R., Raun, W. R. and Stone, M.L. 2006. Spectral Reflectance Indices as Potential Indirect Selection Criteria for Wheat Yield under Irrigation. Crop Sci., 46: 578–588.
4. Barton, C. V. M. 2001. A Theoretical Analysis of the Influence of Heterogeneity in Chlorophyll Distribution on Leaf Reflectance. Tree Physiol., 21: 789–795.
5. Bergsträsser, S., Fanourakis, D., Schmittgen, S., Cendrero-Mateo, M. P., Jansen, M., Scharr, H. and Rascher, U. 2015. HyperART: Non-invasive Quantification of Leaf Traits Using Hyperspectral Absorption-reflectance-transmittance Imaging. Plant Method., 11: 1. doi:10.1186/s13007-015-0043-0.
6. Botha, E. J., Zebarth, B. J. and Lebon, B. 2005. Non-destructive Estimation of Potato Leaf Chlorophyll and Protein Contents from Hyperspectral Measurements Using the PROSPECT Radiative Transfer Model. Can. J. Plant Sci., 86: 279-291.
7. Carter, G. A., Cibula, W. G. and Miller, R. L. 1996. Narrow-band Reflectance Imagery Compared with Thermal Imagery for Early Detection of Plant Stress. J. Plant Physiol., 148: 515–522.
8. Chang, J. H. and Root, B. 1975. On the Relationship between Mean Monthly Global Radiation and Air Temperature. Arch. Meteorol. Geophys. Bioclimatol. B., 23:13–30.
9. Chu, X. L., Yuan, H. F. and Lu, W. Z. 2004. Progress and Application of Spectral Data Pretreatment and Wavelength Selection Methods in NIR Analytical Technique. Progress in Chemistry, 16: 528-542.
10. Daughtry, C. S. T., Walthall, C. L., Kim, M. S., de Colstoun, E. B. and McMurtrey, J. E. 2000. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sens. Environ., 74: 229-239.
11. De Castro, A. I., Jurado-Expόsito, M., Gόmez-Casero, M. T. and Lόpez-Granados, F. 2012. Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops. Scientific World J., 1–11. doi:10.1100/2012/630390.
12. Demirel, K., Genc, L., Bahar, E., Inalpulat, M., Smith, S. and Kizil, U. 2014. Yield Estimate Using Spectral Indices in Eggplant and Bell Pepper Grown under Deficit Irrigation. Fresenius Environ. Bull., 23: 1232-1237.
13. Dhanoa, M. S., Lister, S. J., Sanderson, R. and Barnes, R. 1994. The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra. J. Near Infrared Spectrosc., 2: 43–47.
14. Diller, M. 2002. Investigations for the Development of a NIRS-method for Potatoes in Organic Farming with Special Reference to the Influence of the Year and the Potato Pine. PhD Thesis, Rheinische Friedrich-Wilhelms-Universitat, Bonn, Germany. (in German)
15. Furuuchi, H., Jenkins, M. W., Senock, R. S., Houpis, J. L. C. and Pushnik, J. C. 2013. Estimating Plant Crown Transpiration and Water Use Efficiency by Vegetative Reflectance Indices Associated with Chlorophyll Fluorescence. Open Ecol. J., 3: 122-132.
16. Gamon, J. A. and Surfus, J. S. 1999. Assessing Leaf Pigment Content and Activity with a Reflectometer. New Phytol., 143: 105–117.
17. Genc, L., Demirel, K., Camoglu, G., Asik, S. and Smith, S. 2011. Determination of Plant Water Stress Using Spectral Reflectance Measurements in Watermelon (Citrullus vulgaris). Amer-Eur. J. Agri. Environ. Sci., 11: 296-304.
18. Gitelson, A. A. 2004. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. J. Plant Physiol., 161: 165–173.
19. Gitelson, A. A., Keydan, G. P. and Merzlyak, M. N. 2006. Three-band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves. J. Geophys. Res. Letter, 33(L11402): 5. DOI: 10.1029/2006GL026457.
20. Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C. and Arkebauer, T. J. 2005. Remote Estimation of Canopy cChlorophyll Content in Crops. J. Geophys. Res. Lett., 32: L08403. http://dx.doi.org/10.1029/2005 GL022688.
21. Guang, L. and Liu, X. N. 2009. Two Kinds of Modified Spectral Indices for Retrieval of Crop Canopy Chlorophyll Content. Adv. Earth Sci., 29: 548–554.
22. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J. and Dextraze, L. 2002. Integrated Narrow-band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture. Remote Sens. Environ., 81: 416-426.
23. He, Y., Li, X. and Deng, X. 2007. Discrimination of Varieties of Tea Using Near Infrared Spectroscopy by Principal Component Analysis and BP Model. J. Food Eng., 79: 1238–1242.
24. Herrman, I., Shapira, U., Kinast, S., Karnielli, A. and Bonfil, D. J. 2013. Ground-level Hyperspectral Imagery for Detecting Weeds in Wheat Fields. Precis. Agric., 14: 637-659.
25. Hiscox, J. D. and Israelstam, G. F. 1979. A Method for the Extraction of Chlorophyll from Leaf Tissue without Maceration. Can. J. Bot., 57: 1332-1334.
26. Homayoun, H., Daliri, M. S. and Mehrabi, P. 2011. Effect of Drought Stress on Leaf Chlorophyll in Corn Cultivars (Zea mays). Middle East J. Sci. Res., 9: 418-420.
27. Jin, X., Wang, K., Xiao, C., Diao, W., Wang, F., Chen, B. and Li. S. 2012. Comparison of Two Methods for Estimation of Leaf Total Chlorophyll Content Using Remote Sensing in Wheat. Field Crop Res., 135: 24-29.
28. Jordan, C. F. 1969. Derivation of Leaf Area Index from Quality of Light on the Forest Floor. Ecol., 50: 663–666.
29. Kahrıman, F. 2013. Investigation of Yield and Quality Traits in Maize by Source Sink Releationship and Physiological Parameters. Çanakkale Onsekiz Mart University, Çanakkale, Turkey. (in Turkish)
30. Kaufmann, H., Segl, K., Itzerott, S., Bach, H., Wagner, A., Hill, J., Heim, B., Oppermann, K., Heldens, W., Stein, E., Müller, A., Van der Linden, S., Leitão, P. J., Rabe, A. and Hostert, P. 2010. Hyperspectral Algorithms: Report of the Frame of EnMAP Preparation Activities. 268 S. Scientific Technical Report, STR 10/08, Deutsches GeoForschungsZentrum GFZ, Potsdam. DOI: 10.2312/GFZ.b103-10089.
31. Liu, Z., Zuo, M. J. and Xu, H. 2012. Parameter Selection for Gaussian Radial Basis Function in Support Vector Machine Classification. In: “Proceedings of International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering”, IEEE, 15–18 June 2012, Piscataway, NJ, Chengdu, China.
32. Kong, W., Zhang, C., Liu, F., Nie, P. and He, Y. 2013. Rice Seed Cultivar Identification Using Near-infrared Hyperspectral Imaging and Multivariate Data Analysis. Sensor., 13: 8916-8927.
33. Martin, M.P., Barreto, L., Riaño, D., Fernandez-Quintanilla, C. and Vaughan, P. 2011. Assessing the Potential of Hyperspectral Remote Sensing for the Discrimination of Grassweeds in Winter Cereal Crops. Int. J. Remote Sens., 32: 49-67.
34. Özyiğit, Y. and Bilgen, M., 2013. Use of Spectral Reflectance Values for Determining Nitrogen, Phosphorus, and Potassium Contents of Rangeland Plants. J. Agr. Sci. Tech., 15: 1537-1545.
35. Penuelas, J., Baret, F. and Filella, I. 1995. Semi-Empirical Indices to Assess Carotenoids/Chlorophyll a Ratio from Leaf Spectral Reflectance. Photosynthetica, 31: 221–230.
36. Penuelas, J., Gamon, J.A., Fredeen, A. L., Merino, J. and Field, C. 1994. Reflectance Indices Associated with Physiological Changes in Nitrogen and Water-limited Sunflower Leaves. Remote Sens. Environ., 48: 135-146.
37. Pfitzner, K., Bartolo, R., Carr, G., Esparon, A. and Bollhöfer, A. 2011. Standards for Reflectance Spectral Measurement of Temporal Vegetation Plots. Supervising Scientist Report 195, Supervising Scientist, Darwin NT.
38. Rundquist, D., Gitelson, A., Leavitt, B., Zygielbaum, A., Perk, R. and Keydan, G. 2014. Elements of an Integrated Phenotyping System for Monitoring Crop Status at Canopy Level. Agron., 4: 108-123.
39. Sims, D. A. and Gamon, J. A. 2002. Relationship between Leaf Pigment Content and Spectral Reflectance across a Wide Range Species, Leaf Structures and Development Stages. Remote Sens. Environ., 81: 337–354.
40. Slaton, M. R., Hunt, E. R. and Smith, W. K. 2001. Estimating Near-infrared Leaf Reflectance from Leaf Structural Characteristics. Am. J. Bot., 88: 278-284.
41. Strachan, I.B., Pattey, E. and Boisvert, J.B. 2002. Impact of Nitrogen and Environmental Conditions on Corn as Detected by Hyperspectral Reflectance. Remote Sens. Environ., 80: 213–224.
42. Tucker, C. J. 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sens. Environ., 8: 127-150.
43. Wang, Q. and Li, P. 2012. Hyperspectral Indices for Estimating Leaf Biochemical Properties in Temperate Deciduous Forests: Comparison of Simulated and Measured Reflectance Data Sets. Ecol. Indic., 14: 56–65.
44. Weber, V. S., Araus, J. L., Cairns, J. E., Sanchez, C., Melchinger, A. E., and Orsini, E. 2012. Prediction of Grain Yield Using Reflectance Spectra of Canopy and Leaves in Maize Plants Grown under Different Water Regimes. Field Crop Res., 128: 82–90.
45. Xu, Z., Zhou, G., Han, G. and Li, Y. 2011. Photosynthetic Potential and Its Association with Lipid Peroxidation in Response to High Temperature at Different Leaf Ages in Maize. J. Plant Growth Regul., 30: 41-50.
46. Yi, Q., Huang, J., Wang, F. and Wang, X. 2008. Quantifying Biochemical Variables of Corn by Hyperspectral Reflectance at Leaf Scale. J. Zhejiang Univ-Sci. B, 9: 378-384.
Journal of Agricultural Science and Technology
Volume 18, Issue 6
November and December 2016
Pages 1705-1718