Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants

Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : ?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker
DOI :  10.14445/22315381/IJETT-V69I3P205

Citation 

MLA Style: ?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker  "Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants" International Journal of Engineering Trends and Technology 69.3(2021):23-28. 

APA Style:?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker. Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants  International Journal of Engineering Trends and Technology, 69(3),23-28.

Abstract
Present research was conducted to define the interrelation between the RGB colors of contact imaging and the leaf chlorophyll content of the strawberry plants and to evaluate the expediency of support vector machine (SVM) in the estimation of leaf chlorophyll contents. A closed box was developed with a small hole (8 mm x 8 mm) on, and a red LED source was placed inside the box. A total of 30 leaves were hand collected from 10 different strawberry plantations, and they were placed on the hole one by one. A smartphone was used to capture the contact image of samples and to determine the RGB colors. The destructive determination of the leaf chlorophyll contents was then determined by using standard methods at spectrophotometer. Finally, the RGB color values were treated with a support vector machine for the evaluation of leaf chlorophyll content. In training the data, R-mean, G-mean, B-mean values were used as the input parameters and level of chlorophyll as the output. In support vectors, the model is tested with two different kernels: radial kernel and linear kernel. Results suggested that the estimation of leaf chlorophyll content of strawberry plants through the SVM method could be effective by using RGB colors of contact imaging.

Reference
[1] ?. Kahramano?lu, Introductory Chapter: Postharvest Physiology and Technology of Horticultural Crops. In: Kahramano?lu. ?. editor. Postharvest handling. London, InTech Open, (2017) 1-5.
[2] FAO, Food and Agriculture Organization - Hunger and food insecurity; http://www.fao.org/hunger/en/; Accessed on 15th of April, 2020.
[3] U. Mc Carthy, I. Uysal, R. Badia-Melis, S. Mercier, C. O`Donnell, A. Ktenioudaki, Global food security–issues, challenges, and technological solutions. Trends in Food Science & Technology, 77 (2018) 11-20.
[4] R. Gebbers, V.I. Adamchuk, Precision agriculture, and food security. Science, 327(2010) 828-831.
[5] S. Christensen, E. Nordbo, T. Heisel, A.M. Walter, Overview of developments in precision weed management, issues of interest and future directions being considered in Europe. Precision Weed Management of Crops and Pastures. Adelaide, Australia: CRC for Weed Management Systems, (1998) 3-13.
[6] Y. Karimi, S.O. Prasher, H. McNairn, R.B. Bonnell, P. Dutilleul, P.K. Goel, the Classification accuracy of discriminant analysis, artificial neural networks, and decision trees for weed and nitrogen stress detection in corn. Transactions of the ASAE, 48(3) (2005) 1261-1268.
[7] C. Delloye, M. Weiss, P. Defourny, Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems. Remote Sensing of Environment, 216(2018) 245-261.
[8] D. Jacob?Wilk, D. Holland, E.E. Goldschmidt, J. Riov, Y. Eyal, Chlorophyll breakdown by chlorophyllase: isolation and functional expression of the Chlase1 gene from ethylene?treated Citrus fruit and its regulation during development. The Plant Journal, 20(6)(1999) 653-661.
[9] E. Alós, M. Cercós, M.J. Rodrigo, L. Zacarías, M. Talón, Regulation of color break in citrus fruits. Changes in pigment profiling and gene expression induced by gibberellins and nitrate, two ripening retardants. Journal of Agricultural and Food Chemistry, 54(13)(2006) 4888-4895.
[10] S.P. Yadav, Y. Ibaraki, S.D. Gupta, Estimation of the chlorophyll content of micropropagated potato plants using RGB-based image analysis. Plant Cell, Tissue and Organ Culture (PCTOC), 100(2)(2010) 183-188.
[11] J.E. Sawyer, D.W. Barker, J.P. Lundvall, Using chlorophyll meter readings to determine N application rates for corn. (2004) 136.
[12] R.F. Muñoz-Huerta, R.G. Guevara-Gonzalez, L.M. Contreras-Medina, I. Torres-Pacheco, J.Prado-Olivarez, R.V. Ocampo-Velazquez, A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. sensors, 13(8) (2013) 10823-10843.
[13] B.M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K.I. Theron, J. Lammertyn, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest biology and technology, 46(2)(2017) 99-118.
[14] Y. Song, G. Teng, Y. Yuan, T. Liu, Z. Sun, Assessment of wheat chlorophyll content by the multiple linear regression of leaf image features. Information Processing in Agriculture (2020).
[15] Y. Miao, D.J. Mulla, G.W. Randall, J.A. Vetsch, R. Vintila, Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10(1)(2009) 45-62.
[16] P.K. Goel, S.O. Prasher, R.M. Patel, J.A. Landry, R.B. Bonnell, A.A. Viau, Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture, 39(2)(2003) 67-93.
[17] V. Vapnik, The nature of statistical learning theory. Springer Science & Business Media (2013).
[18] T. Rumpf, A.K. Mahlein, U. Steiner, E.C. Oerke, H.W. Dehne, L. Plümer, Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture, 74(1)(2010) 91-99.
[19] Y. Karimi, S.O. Prasher, R.M. Patel, S.H. Kim, Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and electronics in agriculture, 51(1-2)(2006) 99-109.
[20] S.P. Patil, R.S. Zambre, Classification of cotton leaf spot disease using support vector machine. International Journal of Engineering Research, 3(4)(2014) 1511-1514.
[21] S. Ganguly, K. Bandyopadhyay, R. Dasgupta, Development and Characterization of Bio color Fortified Yogurt: A New Pathway towards Functional Foods. International Journal of Engineering Trends and Technology, 68(4)(2020)1-6.
[22] N. Özreçbero?lu, ?. Kahramano?lu, Mathematical models for the estimation of leaf chlorophyll content based on RGB colors of contact imaging with smartphones: A pomegranate example. Folia Horticulturae, 32(1)(2020) 57-67.
[23] Adobe. https://www.adobe.com/products/photoshop.html (2021).
[24] P. Sudhakar, P. Latha, P.V. Reddy, Chapter 15 - Plant pigments. In: Sudhakar P, Latha P, Reddy PV, editors. Phenotyping Crop Plants for Physiological and Biochemical Traits. Academic Press, Elsevier Inc; (2016) 121-127.
[25] M. Roca, K. Chen, A. Pérez-Gálvez, Chapter 6: Chlorophylls. In: Carle, R., Schweiggert, R.M. editors. Handbook on Natural Pigments in Food and Beverages, Industrial Applications for Improving Food Color. Academic Press, Elsevier Inc; (2016) 125-158.
[26] R.L. Rorie, L.C. Purcell, M. Mozaffari, D.E. Karcher, C.A. King, M.C. Marsh, D.E. Longer, Association of “greenness” in corn with yield and leaf nitrogen concentration. Agronomy Journal, 103(2)(2011) 529-535.
[27] V.K. Tewari, A.K. Arudra, S.P. Kumar, V. Pandey, N.S. Chandel, Estimation of plant nitrogen content using digital image processing. Agricultural Engineering International: CIGR Journal, 15(2)(2013) 78-86.
[28] A.K. Dey, M. Sharma, M.R. Meshram, An analysis of leaf chlorophyll measurement method using chlorophyll meter and image processing technique. Procedia Computer Science, 85(2016) 286-292.
[29] F. Vesali, M. Omid, A. Kaleita, H. Mobli, Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture, 116(2015) 211-220.
[30] J.P.G. Rigon, S. Capuani, D.M. Fernandes, T.M. Guimarães, A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica, 54(4)(2016) 559-566.
[31] F. Vesali, M. Omid, H. Mobli, A. Kaleita, Feasibility of using smartphones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4)(2017) 603-610.

Keywords
Contact imaging, precise agriculture, RGB colors, smartphone, support vector machines