摘要： This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement. In our experimental design, cherry seedlings were grown under various N deficiency conditions in nutrient-controlled containers. The reflectance values of plant leaves were measured using a spectro-radiometer. Plant leaves samples were simultaneously collected. Their nutrient contents were determined in the laboratory. Afterwards, we performed a statistical comparison of the reflectance values. Sample analysis results established the significant wavelengths. Moreover, we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions. Next, we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer. Nutrient deficiencies were calculated using the developed model, and then, the predicted and measured data were compared to evaluate model validity. From these results, we determined the wavelengths that yielded the most accurate results for N prediction, selected from the blue and green regions of the spectrum. We established that for N prediction in cherry trees, the simplest model can be created using 560 and 570 nm wavelengths. However, the evaluated model can be applicable only under certain conditions. We concluded that in order to develop a prediction method with sufficient application capacity, as well as the ability to assess nutritional and physiological characteristics, the ecology condition of the plant should be properly considered based on the model.
关键词：Cherry;Handheld field spectroradiometer;Hyperspectral sensing;N deficiency;Reflectance
Abstract：This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement. In our experimental design, cherry seedlings were grown under various N deficiency conditions in nutrient-controlled containers. The reflectance values of plant leaves were measured using a spectro-radiometer. Plant leaves samples were simultaneously collected. Their nutrient contents were determined in the laboratory. Afterwards, we performed a statistical comparison of the reflectance values. Sample analysis results established the significant wavelengths. Moreover, we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions. Next, we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer. Nutrient deficiencies were calculated using the developed model, and then, the predicted and measured data were compared to evaluate model validity. From these results, we determined the wavelengths that yielded the most accurate results for N prediction, selected from the blue and green regions of the spectrum. We established that for N prediction in cherry trees, the simplest model can be created using 560 and 570 nm wavelengths. However, the evaluated model can be applicable only under certain conditions. We concluded that in order to develop a prediction method with sufficient application capacity, as well as the ability to assess nutritional and physiological characteristics, the ecology condition of the plant should be properly considered based on the model.
Key words：Cherry;Handheld field spectroradiometer;Hyperspectral sensing;N deficiency;Reflectance
Levent Basayigit1, Mert Dedeoglu2, Hüseyin Akgül3, Kadir Ucgun3, Mesut Altndal3 . Investigation of N Deficiency in Cherry Trees Using Visible and Near-Infrared Spectra Part of the Spectrum in Field Condition[J]. 光谱学与光谱分析, 2017, 37(01): 293-298.
Levent Basayigit1, Mert Dedeoglu2, Hüseyin Akgül3, Kadir Ucgun3, Mesut Altndal3 . Investigation of N Deficiency in Cherry Trees Using Visible and Near-Infrared Spectra Part of the Spectrum in Field Condition. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(01): 293-298.
[1] Chang C W, Laird D A, Mausbach M J，et al. Soil Sci. Soc. of Am. J，2001，65: 480. [2] Pasquini C. J. Braz. Chem. Soc.，2003，14: 198. [3] Viscarra Rossel R A, Walvoort D J J, Mcbratney A B, et al. Visible, Near Infrared, Mid-Infrared or Combined Diffuse Reflectance Spectroscopy for Simultaneous Assessment of Various Soil Properties Geoderma，2006，131: 59. [4] Merzlyak M N, Giletson A A, Chivkunova O B, et al. Russian Journal of Plant Physiology，2003，50: 704. [5] Dusseux P, Vertes F, Corpetti T, et al. Environ. Monit. Assess.，2014，186: 8249. [6] Lin C, Popescu S C, Huang S C, et al. Biogeosciences, 2015, 12: 49. [7] Genot V, Colinet G, Bock L, et al. Journal of Near Infrared Spectroscopy，2011，19(21): 17. [8] José A M, Demattê H, Bellinaso S, et al. Revista Ciência Agronmica，2016，47(4): 589. [9] Jacquemoud S，Ustin S I. Leaf Optical Properties: A State of the Art. Proc. 8th Int. Symp. Phyisical Measurements and Signatures in Remote Sensing. Aussois, France, Jan. 8-12. CNES, 2001. 223. [10] Rao P, Hutyra L R, Raciti S M，et al. Urban Ecosyst，2013，16: 593. [11] Laudien R, Bareth G，Doluschitz R. Analysis Hyper Spectral Field Data for Detection of Sugar Beet Diseas. EFITA 2003 Conference, Deprecen. Hungary，2003. [12] Bandyopadhyay K K, Pradhan S, Sahoo R N, et al. Agric. Water Manag.，2014，146: 115. [13] Penuelas J，Filella I. Trends Plant Sci.，1998，3: 151. [14] Zhao K G, Valle D, Popescu S, et al. Remote Sens. Environ.，2013，132: 102. [15] Zhao D, Reddy K R, Kakani V G, et al. Agronomy Journal, 2005，97(1)：89. [16] Croft H，Chen J M，Zhang Y. Ecol. Complex.，2014, 17：119. [17] Basayigit L, Albayrak S, Senol H，et al. Determinability of Plant Nutrient Elements through Spectroradiometer Data. 4th National Plant Nutrition and Fertilizer Conference, Konya，2008. [18] Dedeoglu M，Basayigit L. Spectrosc. Spectr. Anal.，2015，35(1): 355. [19] Kacar B，nal A. Plant Analysis. Nobel Publication, No:1241, 892, ISBN 978-605-395-036-3, Ankara，2008. [20] Jones J R, Wolf B，Mills H A. Plant Analysis Handbook, Micro Macro Publishing Inc. 1991. [21] Graeff S, Steffens D, Schubert S. J. Plant Nutrient Soil Science，2001，164: 445. [22] Silva T A，Beyl C A. Advances in Spaces Research, COSPAR Publication，2005，35: 305. [23] Basayigit L, Albayrak S，Senol H. Asian Journal of Chemistry，2009，21: 1302. [24] Jain N，Ray S. Springer Science+Business Media，2007，8: 225. [25] Blackburn G A. Journal of Experimental Botany，2006，58: 855. [26] Basayigit L，Senol H. International Journal of ChemTech Research，2009，1(2): 212.
