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Location of Sampling Points in Optical Reflectance Measurements of Chinese Cabbage and Kale Leaves

  • Ngo, Viet-Duc (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Kang, Sin-Woo (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Ryu, Dong-Ki (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Chung, Sun-Ok (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Park, Sang-Un (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Kim, Sun-Ju (College of Agriculture and Life Sciences, Chungnam National University) ;
  • Park, Jong-Tae (College of Agriculture and Life Sciences, Chungnam National University)
  • Received : 2015.01.28
  • Accepted : 2015.05.22
  • Published : 2015.06.01

Abstract

Purpose: A sampling scheme may significantly affect the accuracy of a sensor. This study was conducted to investigate the effects of sampling point locations on optical reflectance measurements of Chinese cabbage and kale plant leaves. Methods: Variability and similarity of multiple measurements for different parts of the leaves were compared. Results: The results indicate that the variability between the average and individual reflectance spectra was smaller for the blade part than for the vein part. Furthermore, the reflectance for the blade part over the upper leaf area was greater and more stable than those for the other parts for both the cabbage and kale leaf samples. Conclusions: The results provide guidelines for optical reflectance measurements of Chinese cabbage and kale plants. The effects of the number of sampling points, the number of leaves, and the relationships between optical reflectance and leaf components remain to be investigated in the future.

Keywords

References

  1. Andrew, D. R. and P. B. Graeme. 2002. Changes in foliar spectral reflectance and chlorophyll fluorescence of four temperate species following branch cutting. Journal of Tree Physiology 22:499-506. https://doi.org/10.1093/treephys/22.7.499
  2. Charles, A. S. 1929. A spectrophotometric study of reflection of light from leaf surfaces. Botanical Gazette 87(5): 583-607. https://doi.org/10.1086/333965
  3. Davenport, J. R., E. M. Perry, N. S. Lang and R. G. Stevens. 2005. Leaf spectral reflectance for non-destructive measurement of plant nutrient status. HortTechnology 15(1):31-35.
  4. Follett, R. H., R. F. Follett and A. D. Halvorson. 1992. Use of a chlorophyll meter to evaluate the nitrogen status of dry land winter wheat. Communications in Soil Science and Plant Analysis 23:687-697. https://doi.org/10.1080/00103629209368619
  5. Gitelson, A. A., M. N. Merzlyak and H. K. Lichtenthaler. 1996. Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology 148:501-508. https://doi.org/10.1016/S0176-1617(96)80285-9
  6. Hu, C. G., L. D. Sun, J. M. Flores-Camacho, M. Hohage, C. Y. Liu and X. T. Hu. 2010. A rotating-compensator based reflectance difference spectrometer for fast spectroscopic measurements. Review of Scientific Instruments 81(043108):1-11.
  7. Jensen, J. R. 2000. Remote Sensing of the Environment an Earth Resource Perspective, ch. 10, Prentice-Hall, USA.
  8. Kathryn, T. M. and G. Smith. 2010. Korea-Republic of cabbage market trends. Global Agricultural Information Network, Report categories: Agricultural Situation; GAIN report number: KS1027.
  9. La, G. X., P. Fang, Y. B. Teng, Y. J. Li and X. Y. Lin. 2009. Effect of $CO_2$ enrichment on the glucosinolate contents under different nitrogen levels in bolting stem of Chinese kale (Brassica alboglabra L.). Journal of Zhejiang University Science B 10(6):454-464. https://doi.org/10.1631/jzus.B0820354
  10. Lee, H. I. and Y. H. Kim. 2013. Utilization efficiencies of electric energy and photosynthetically active radiation of lettuce grown under red LED, blue LED and fluorescent lamps with different photoperiods. Journal of Biosystems Engineering 38(4):279-286. https://doi.org/10.5307/JBE.2013.38.4.279
  11. Li, R., S. Kawamura, H. Fuijita and S. Fujikawa. 2013. Near-infrared spectroscopy for determining grain constituent contents. Engineering in Agriculture, Environment and Food 6(1):20-26. https://doi.org/10.1016/S1881-8366(13)80013-4
  12. Liu, N., Z. F. Lin, A. V. Devender, G. Z. Lin, C. L. Peng, X. P. Pan, S. W. Chen and Q. Gu. 2009. Spectral reflectance indices and pigment functions during leaf ontogenesis in six subtropical landscape plants. Journal of Plant Growth Regulation 58:73-84. https://doi.org/10.1007/s10725-008-9353-9
  13. Luhimi, S., C. Y. Mo, J. S. Kang, S. J. Hong and B. K. Cho. 2013. Nondestructive evaluation for the viability of watermelon (Citrullus lanatus) seeds using fourier transform near infrared spectroscopy. Journal of Biosystems Engineering 38(4):312-317. https://doi.org/10.5307/JBE.2013.38.4.312
  14. Lyon, D. A. 2010. The discrete fourier transform, part 6: Cross-correlation. Journal of Object Technology 9(2): 17-22. https://doi.org/10.5381/jot.2010.9.2.c2
  15. Noichinda, S., K. Bodhipadma, C. Mahamontri, T. Narongruk and S. Ketsa. 2007. Light during storage prevents loss of ascorbic acid, and increases glucose and fructose levels in Chinese kale (Brassica oleracea var. alboglabra). Postharvest Biology and Technology 44:312-315. https://doi.org/10.1016/j.postharvbio.2006.12.006
  16. Norris, K. H. and P. C. Williams. 1984. Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat. I. Influence of particle size. Cereal Chemistry 61(2):158-165.
  17. Penuelas, J. and Y. Inoue. 1999. Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36(3): 355-360. https://doi.org/10.1023/A:1007033503276
  18. Pfitzner, K., A. Bollhofer and G. Carr. 2006. A standard design for collecting vegetation reference spectra: Implementation and implications for data sharing. Journal of Spatial Science 52(2):79-92.
  19. Rinnan, A., F. V. D. Berg and B. Engelsen. 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28(10):1201-1222. https://doi.org/10.1016/j.trac.2009.07.007
  20. Savitzky, A. and M. J. E. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36(8):1627-1639. https://doi.org/10.1021/ac60214a047
  21. Suh, S. R., K. H. Lee, S. H. Yu, H. S. Shin, Y. S. Choi and S. N. Yoo. 2012. A melon fruit grading machine using a miniature VIS/NIR spectrometer: 1. calibration models for the prediction of soluble solids content and firmness. Journal of Biosystems Engineering 37(3):166-176. https://doi.org/10.5307/JBE.2012.37.3.166
  22. Suhandy, D., T. Suzuki, Y. Ogawa, N. Kondo, H. Naito, T. Ishihara, Y. Takemoto and W. Liu. 2012. A quantitative study for determination of glucose concentration using attenuated total reflectance terahertz (ATR-THz) spectroscopy. Engineering in Agriculture, Environment and Food 5(3):90-95. https://doi.org/10.1016/S1881-8366(12)80020-6
  23. Sun, D. W. 2009. Infrared spectroscopy for food quality analysis and control. Part I: Fundamentals and instruments, Section 2: Data pre-processing, 29-48, Elsevier Inc, USA.
  24. Wang, Q., J. Chen and Y. Li. 2004. Non-destructive and rapid estimation of leaf chlorophyll and nitrogen status of peace lily using a chlorophyll meter. Journal of Plant Nutrition 27(3):557-569. https://doi.org/10.1081/PLN-120028878
  25. Wu, D., L. Feng, Y. He and Y. Bao. 2008. Variety identification of Chinese cabbage seeds using visible and near-infrared spectroscopy. Transactions of the ASABE 51(6):2193-2199. https://doi.org/10.13031/2013.25382
  26. Zahran, M. A. and A. J. Willis. 2009. The Vegetation of Egypt: 2nd edition, ed. M.J.A. Werger, ch. 8, 319-333. Springer Science+Business Media B.V.