Image Identification Technology of Branches in Loquat Trees

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Abstract:

The pruning of branches in loquat trees is the main part of the consumption of labor in production, so the use of branch intelligent pruning device can effectively reduce the labor intensity and cost. Identification of branches is the key technology of intelligent pruning branches, and it is the first step of intelligent pruning equipment research. A recognition method of branch images was introduced in this paper. With this approach, we used open operation to deal with the images after the segmentation which was performed by using color aberration, and the image noises were eliminated basing on comparison of connected areas. For faulted branches which were caused by leaves shield or strong illumination, they could be filled by expanding at the designated place, and then the integrated frameworks of branch images were obtained. At last, both the positions of the center points and the diameters of loquat branch images were determined based on the characteristics of the branch image edges. Experiments showed that the accurate identification rate of branch feature images and branch center coordinates were 89.3% and 84.6%.

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213-217

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October 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] J.A. Scarpare: Rev. Bra. Frut. Vol. 35 (2013), p. Iii.

Google Scholar

[2] D.M. Glenn and E. Campostrini: Sci. Hortic-Amsterdam, Vol. 129 (2011), p.889.

Google Scholar

[3] V. Mercier, C. Bussi, D. Plenet, and F. Lescourret: Crop. Proy. Vol. 27(2008), p.678.

Google Scholar

[4] T. Olesen, C.M. Menzel, C.A. McConchie, and N. Wiltshire: Sci. Hortic-Amsterdam. Vol. 156(2013), p.93.

Google Scholar

[5] T.J. Tworkoski, and D.M. Glenn: Sci. Hortic-Amsterdam. Vol. 126(2010), p.130.

Google Scholar

[6] M. Karkee, B. Adhikari, S. Amatya, and Q. Zhang: Comput. Electron. Agr. Vol. 103(2014), p.127.

Google Scholar

[7] Z. De-An, L. Jidong, J. Wei, Z. Ying, and C. Yu: Biosyst. Bioeng, Vol. 110(2011), p.112.

Google Scholar

[8] J. Blasco, N. Aleixos, J. Gómez, and E. Moltó: J. Food Eng. Vol. 83(2007), p.384.

Google Scholar

[9] P.M. Granitto, H.D. Navone, P.F. Verdes, and H.A. Ceccatto: Comput. Electron. Agr. Vol. 33(2002), p.91.

Google Scholar

[10] P. Vanloot, D. Bertrand, C. Pinatel, J. Artaud, and N. Dupuy: Comput. Electron. Agr. Vol. 102(2014), p.98.

Google Scholar

[11] M. Zhang, and Q. Meng: Patter. Recogn. Lett. Vol. 32(2011), p. (2036).

Google Scholar

[12] S. Zhang, B. Li, Y. Liu, L. Zhang, Z. Wang, and M. Han: Sci. Hortic-Amsterdam. Vol. 130(2011), p.102.

Google Scholar

[13] N. Otsu: Man and Cybernetics Vol. SMC-9(1979), p.62.

Google Scholar