摘要： Visible and near-infrared (VNIR) spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies. The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions. The study was conducted in 3 different locations in Isparta region of Turkey. Fifteen cherry orchards containing normal and Zn deficient plants were chosen, and 60 leaf samples were collected from each location. The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe. The Zn contents of leaf samples were predicted through laboratory analysis. The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method. Prediction models were created using the highest coefficient of determination value. The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method (87.5<r2<96.79). Moreover, plant nutrient contents can be estimated without using chemicals. However, further research is necessary to develop a standard method for field conditions. Because spectral reflectance is affected by ecological conditions, agricultural applications and nutrient interactions, more effective models must be developed depending on the geographical location, period and plant type.
关键词：Cherry;Spectroradiometer;Stepwise Regression;Visible and Near Infrared (VNIR);Zn content
Abstract：Visible and near-infrared (VNIR) spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies. The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions. The study was conducted in 3 different locations in Isparta region of Turkey. Fifteen cherry orchards containing normal and Zn deficient plants were chosen, and 60 leaf samples were collected from each location. The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe. The Zn contents of leaf samples were predicted through laboratory analysis. The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method. Prediction models were created using the highest coefficient of determination value. The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method (87.5<r2<96.79). Moreover, plant nutrient contents can be estimated without using chemicals. However, further research is necessary to develop a standard method for field conditions. Because spectral reflectance is affected by ecological conditions, agricultural applications and nutrient interactions, more effective models must be developed depending on the geographical location, period and plant type.
Key words：Cherry;Spectroradiometer;Stepwise Regression;Visible and Near Infrared (VNIR);Zn content
通讯作者:
Mert Dedeoglu
E-mail: mertdedeoglu@msn.com; leventbasayigit@hotmail.com
引用本文:
Mert Dedeoglu1, Levent Baayigit2. Determining the Zn Content of Cherry in Field Using VNIR Spectroscopy[J]. 光谱学与光谱分析, 2015, 35(02): 355-361.
Mert Dedeoglu1, Levent Baayigit2. Determining the Zn Content of Cherry in Field Using VNIR Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 355-361.
[1] Gomez C, Viscarra Rossel R A，McBratney A B. Geoderma, 2008，146: 403. [2] Ladoni M, Bahrami H, Alavipanah S，et al. Precis. Agric., 2010, 11: 82. [3] Peng X, Shi T, Song A, , et al. Remote Sens. 2014, 6: 2699. [4] Shi T, Chen Y, Liu Y, et al. Journal of Hazardous Materials, 2014, 265: 166. [5] Lillhonga T，Geladi P. J. Chemometrics, 2011, 25(4): 193. [6] Cao H，Zhou Y. Scientific Research and Essay, 2014，9(9): 288. [7] Thomas J R，Gausman H W. Agron. J., 1966，69: 799. [8] Maktav D，Sunar F. Translated from Remote Sensing: A Quantitative Approach by Swain/Davis, Istanbul, 1991. [9] Graeff S, Steffens D，Schubert S. J. Plant Nutrient, Soil Science, 2001，164: 445. [10] Christensen L, Bennedsen B, Jrgensen R，et al. Biosystem Engineering, 2004. [11] Silva T A，Beyl C A. Advances in Spaces Research, COSPAR Publication, 2005，35: 305. [12] Blackburn G A. Journal of Experimental Botany, 2006，58(4): 855. [13] Jain N，Ray S. Springer Science+Business Media，2007，8: 225. [14] Basayigit L, Albayrak S，Senol H. Asian Journal of Chemistry，2009，21(2): 1302. [15] Abadía J, Vázquez S, lvarez R R, et al. Plant Physiology and Biochem, 2011, 49:471. [16] Chang C W, Laird D A, Mausbach M J，et al. Soil Science Society of American Journal, 2001, 65: 480. [17] Pasquini C. The Journal of the Brazilian Chemical Society, 2003，14(2): 198. [18] Penuelas J，Filella I. Trends Plant Science, 1998，3: 151. [19] Zhao D, Starks P J, Brown M A, et al. Japanese Society of Grassland Science, 2006，53(1): 39. [20] Yoder B J，Pettigrew-Crosby R E. Remote Sensing of Environment, 1995, 53: 199. [21] Sims D A，Gamon J A. Remote Sensing of Environment，2002，81: 337. [22] Albayrak S, Ba瘙塂ayiit L, Türk M. International Journal of Remote Sensing, 2011，32: 1199. [23] Card D H, Peterson D L, Matson P A, et al. Remote Sensing of Environment，1988，26: 123. [24] Curran P J. Remote Sensing of Environment，1989，30: 271. [25] Wessman C A, Aber J D，Peterson D L. International Journal of Remote Sensing, 1989，10: 1293. [26] Grossman Y L, Ustin S L, Jacquemoud S, et al. Remote Sensing of Environment, 1996, 56: 1. [27] Martin M E，Aber J D. Ecological Applications, 1997，7: 431. [28] Kokaly R F，Clark R N. Remote Sensing of Environment, 1999，67: 267. [29] Curran P J, Dungan J L，Peterson D L. Remote Sensing of Environment, 2001，76(3): 349. [30] Vávová P, Stenberg B, Karsisto M, et al. Near Infrared Reflectance Spectroscopy for Characterization of Plant Litter Quality: Towards a Simpler Way of Predicting Carbon Turnover in Peat lands Wastewater Treatment, Plant Dynamics and Management 65 in Constructed and Natural Wetlands, Springer Science Business Media B.V., 2008. [31] Gholizadeh A, Saberioon M M，Amin M S M. Paddy Soil Nutrient Assessment Using Visible And Near Infrared Reflectance Spectroscopy. Image Processing and Photonics for Agricultural Engineering January 27, Sanya, China, 2013. [32] Pacumbaba R O，Beyl C A. Adv. in Space. Res., 2011，48: 32. [33] Kacar B, Katkat A V，ztürk 瘙塁. Plant Physiology. Nobel Publication, No: 975-564-133-5, Ankara, 2002. [34] Kacar B. Basic Plant Nutrition. Nobel Publication, No: 206,18, ISBN 978-605-133-108-9, Ankara, 2012. [35] Alloway, B J. Zinc in Soil and Crop Nutrition. Pp.1-135. International Zinc Association, International Fertilizer Industry Association. Second edition, published by IZA and IFA Brussels, Belgium and Paris, France, 2008. [36] Rout G R, Das P. Zinc. Agron, 2003，23: 3. [37] Menesatti P, Antonucci F, Pallottino F, et al. Biosyst Eng., 2010，105(4): 448. [38] Shao Y，He Y. Sensors, 2013，13: 1872. [39] Ordóez C, José R P, Moreira J J，et al. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 2610. [40] Ugun K，Gezgin S. SDU Journal of the Faculty of Agriculture, 2013，8: 59. [41] Jones J R, Wolf B，Mills H A. Plant Analysis Handbook, Micro Macro Publishing Inc, 1991. [42] Kacar B，nal A. Plant Analysis. Nobel Publication, No: 1241, 892, ISBN 978-605-395-036-3, Ankara, 2008. [43] Basayigit L，Senol H. International Journal of Chem. Tech. Research, 2009，1(2): 212. [44] Osbourne S L, Schepers J S, Francis D, et al. Agronomy Journal. University of Nebraska Agronomy and Horticulture Department, Agronomy Faculty Publications, 2002, 94(6): 1215.
