An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples

  • Dandan Wang College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
  • Huaibo Song College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
  • Xiuli Yu College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
  • Weiyuan Zhang College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
  • Weifeng Qu College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
  • Yue Xu College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100
DOI: https://doi.org/10.5424/sjar/2015131-6181
Keywords: picking robot, fruit picking point, symmetry axes extraction, moment of inertia, convex hull

Abstract

The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples.

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References

References

Arefi A, Motlagh AM, Mollazade K, Teimourlou RF, 2011. Recognition and localization of ripen tomato based on machine vision. Aust J Crop Sci 5(10): 1144-1149.

Bulanon DM, Kataoka T, Okamoto H, Hata S, 2004. Development of a real-time machine vision system for the apple harvesting robot. SICE Annu Conf in Sapporo, Aug 4-6. pp: 595-598.

Chinchuluun R, Lee WS, Burks TF, 2006. Machine vision-based Citrus yield mapping system. Proc Fla State Hort Soc 119: 142-147.

Fukushima K, Kikuchi M, 2006. Symmetry axis extraction by a neural network. Neuro- computing 69(16): 1827-1836. http://dx.doi.org/10.1016/j.neucom.2005.11.010

Gong JW, Huang WY, Lu JL, 2001. A new algorithm to estimate the symmetrical axis of a curve in data processing. Measurement Technique 6: 3-5.

Guo F, Cao QX, Cui YJ, Nagata M, 2008. Fruit location and stem detection method for strawberry harvesting robot. T CSAE 24(10): 89-94.

Jiménez AR, Ceres R, Pons JL, 2000. A survey of computer vision methods for locating fruit on trees. T ASAE 43(6): 1911-1920. http://dx.doi.org/10.13031/2013.3096

Jim J, Jensen JR, Tullis JA, 2008. Object-based change detection using correlation image analysis and image segmentation. Int J Remote Sens 29(2): 399-423. http://dx.doi.org/10.1080/01431160601075582

Liang N, Guo L, Yu Y, 2009. A symmetry detected method based on the minimal value of moment of inertia. Microprocessors 30(6): 62-64.

Luo MR, Cui G, Rigg B, 2000. The development of the CIE 2000 color-difference formula: CIEDE. Color Res Appl 26(5): 340-350. http://dx.doi.org/10.1002/col.1049

Plebe A, Grasso G, 2001. Localization of spherical fruits for robotic harvesting. Machine Vision Appl 13(2): 70-79. http://dx.doi.org/10.1007/PL00013271

Rajneesh B, Won SL, Saumya S, 2013. Green citrus detection using fast fourier transform leakage. Precis Agric 14(1): 59-70. http://dx.doi.org/10.1007/s11119-012-9292-3

Rekik A, Zribi M, Benjelloun M, Ben HA, 2006. A K-means clustering algorithm initialization for unsupervised statistical satellite image segmentation. E-Learning in Industrial Electronics, 1st IEEE Int Conf, pp: 11-16.

Remy E, Thiel E, 2005. Exact medial axis with euclidean distance. Image Vision Comput 23(2): 167-175. http://dx.doi.org/10.1016/j.imavis.2004.06.007

Song HB, He DJ, Pan JP, 2012. Recognition and localization methods of occluded apples based on convex hull theory. T CSAE 28(22): 174-180.

Song HB, Zhang CD, Pan JP, Yin X, 2013. Segmentation and reconstruction of overlapped apple images based on convex hull. T CSAE 29(3): 163-168.

Tanigaki K, Fujiura T, Akase A, Imagawa J, 2008. Cherry-harvesting robot. Comput Electron Agr 63(1): 65-72. http://dx.doi.org/10.1016/j.compag.2008.01.018

Xiang R, Ying YB, Jiang HY, Peng YS, 2010. Three-dimensional location of tomato based on binocular stereo vision for tomato harvesting robot. 5th Int Symp Advanced Optical Manufacturing and Testing Technologies, Int Soc for Optics and Photonics. pp: 76582Z-76582Z.

Xie ZH, Xu Y, Ji CY, Guo XQ, Zhu SX, 2012. Estimation method of apple growing attitude based on computer vision. T CSAM 42(11): 154-157.

Yao H, Duan Q, Li D, Wang J, 2013. An improved K-means clustering algorithm for fish image segmentation. Math Comput Model 58: 790-798. http://dx.doi.org/10.1016/j.mcm.2012.12.025

Yin HP, Chai Y, Yang SX, Simon X, Mittal GS, 2009. Ripe tomato recognition and localization for a tomato harvesting robotic system. Int Conf of Soft Computing and Pattern Recognition, Melacca, TBD, Malaysia, pp: 557-562.

Zhang HM, Gao MT, Zhang GM, 2010. Symmetry axes search of 2D point set based on convex hull technology. Comput Appl 30(4): 905-908.

Zhou QH, Huang T, Wu HY, Li ZJ, Lin X, 2008. A new algorithm for finding convex hull with a maximum pitch of the dynamical base line. Knowledge Discovery and Data Mining, WKDD. First Int Workshop IEEE, pp: 630-634.

Published
2015-02-12
How to Cite
Wang, D., Song, H., Yu, X., Zhang, W., Qu, W., & Xu, Y. (2015). An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples. Spanish Journal of Agricultural Research, 13(1), e0205. https://doi.org/10.5424/sjar/2015131-6181
Issue
Vol. 13 No. 1 (2015)
Section
Agricultural engineering

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