Studies on the Optical Measuring System for Grape Stem Diameter

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Abstract:

This study presented a novel non-contact optical measuing method for grape stem diameter in fields. The main algorithm contained 3 steps: saperation of the stem from background, determination of the stem segment, and the computation of stem diameter. The experiments conducted in field not only validated the effectiveness, but also proved the high accuracy of the optical measuring system. And our further experimental results showed that the continuous measurement of grape stem diameter can provide periodic data for the analysis of physiological disorders in grapes.

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65-70

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Kopyt, Y. Ton, S. Tsadok, Application of the phytomonitoring technique for scheduling irrigation of golan heights winery's vineyards in 2001-2003, Summary report. Phytech. Ltd, (2004).

Google Scholar

[2] M. Kacira, P.P. Ling, T.H. Short, Machine vision extracted plant movement for early detection of plant water stress, Trans. ASAE 45 (4) (2002) 1147-1153.

DOI: 10.13031/2013.9923

Google Scholar

[3] M. Gallardo, R.B. Thompson, L.C. Valdez, et al., Use of stem diameter variations to detect plant water stress in tomato, Irrigation Science 24 (4) (2006) 241-255.

DOI: 10.1007/s00271-005-0025-5

Google Scholar

[4] Y. Ton, M. Kopyt, I. Zachs, Phytomonitoring technique for tuning irrigation of vineyards, Acta Horticulturae 646 (2002) 133-139.

DOI: 10.17660/actahortic.2004.646.16

Google Scholar

[5] S.O. Link, M.E. Thiede, M.G. van Bavel, An improved strain-gauge device for continuous field measurement of stem and fruit diameter, J. Exp. Bot 49(326) (1998) 1583-1587.

DOI: 10.1093/jxb/49.326.1583

Google Scholar

[6] J. L Barron, A. Liptay, Measuring 3D plant Growth Using Optical Flow, BioImaging 5(2) (1997) 82-86.

DOI: 10.1002/1361-6374(199706)5:2<82::aid-bio5>3.3.co;2-6

Google Scholar

[7] T. Eguchi, T. Araki, M. Kitano, Non-contact measurements of storage organ growth in fruit and root crops, Environ. Control Biol 45(4) (2007) 251-258.

DOI: 10.2525/ecb.45.251

Google Scholar

[8] T. Brosnan, D.W. Sun, Inspection and grading of agricultural and food products by computer vision systems-a review, Comput. Electron. Agric 36(2-3) (2002) 193-213.

DOI: 10.1016/s0168-1699(02)00101-1

Google Scholar

[9] C.F. Chien, T.T. Lin, Non-destructive growth measurement of selected vegetable seedlings using orthogonal images, Trans. ASAE 48(5) (2005) 1953-(1961).

DOI: 10.13031/2013.19987

Google Scholar

[10] C. Tomasi, R. Manduchi, Bilateral Filtering for Gray and Color Images, in: Proceedings of 1998 IEEE ICCP, pp.839-846, Bombay, India.

Google Scholar

[11] N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern 9(1) (1979) 62-66.

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[12] S. Ghosal, R. Mehrotra, A moment-based unified approach to image feature detect, IEEE Trans. Image Processing 6(6) (1997) 781-793.

DOI: 10.1109/83.585230

Google Scholar

[13] Y.D. Qu, C.S. Cui, S.B. Chen, et al., A fast subpixel edge detection method using Sobel-Zernike moment operator, Image Vis. Comput 23(1) (2005) 11-17.

DOI: 10.1016/j.imavis.2004.07.003

Google Scholar

[14] T.J. Bin, A. Lei, C. Jiwen, et al., Subpixel edge location based on orthogonal Fourier-Mellin moments, Image Vis. Comput 26(4) (2008) 563-569.

DOI: 10.1016/j.imavis.2007.07.003

Google Scholar