Multi-template matching algorithm for cucumber recognition in natural environment

Computers and Electronics in Agriculture

Volumn 127, Issue , 2016, Pages 754-762

  Bao, Guanjun ^a   Cai, Shibo ^a   Qi, Liyong ^a   Xun, Yi ^a   Zhang, Libin ^a   Yang, Qinghua ^a  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Cucumber recognition; Harvesting robot; Multiple template matching

Indexed keywords

ALGORITHMS; IMAGE SEGMENTATION; ROBOTS; TEMPLATE MATCHING; VISION;

AUTOMATIC RECOGNITION; CUCUMBER RECOGNITION; HARVESTING ROBOT; IDENTIFICATION ACCURACY; MULTI-TEMPLATE MATCHING; MULTIPLE TEMPLATE MATCHING; NORMALIZED CORRELATION COEFFICIENT; STATISTICAL PARAMETERS;

IMAGE MATCHING;

ACCURACY ASSESSMENT; ALGORITHM; COMPLEXITY; CORRELATION; MORPHOLOGY; ROBOTICS; SEGMENTATION; VINE;

CUCUMIS SATIVUS;

EID: 84982149376     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2016.08.001     Document Type: Article

References (25)

1. Briechle, K., Hanebeck, U.D., Template matching using fast normalized cross correlation. Aerospace/Defense Sensing, Simulation, and Controls, 2001, International Society for Optics and Photonics, 95–102.
2. Clement, J., Novas, N., Gazquez, J.A., Manzano-Agugliaro, F., An active contour computer algorithm for the classification of cucumbers. Comput. Electron. Agric. 92 (2013), 75–81.
3. Jin, L.Z., Tu, J., Liu, C.L., A method for cucumber identification based on iterative-RELIEF and relevance vector machine. J. Shanghai Jiaotong Univ., 47(4), 2013 602 – 606 + 612.
4. Kollenburg, L.M., Werkhoven, C., Wenne-Kes, P., Development of a Mechatronic System for Automatic Harvesting of Cucumbers. Robotics and Automated Machinery for Bio-Productions. 1997, Transactions of the ASAE, Valencia, 21–24.
5. Kondo, N., Nakamura, H., Monta, M., Shibano, Y., Mohri, K., Arima, S., Visual sensor for cucumber harvesting robot. Proceedings of Processing Automation Conference III, 1994, Transactions of the ASAE, Japan, 461–470.
6. Li, E.C., Li, Z.M., Liu, W.R., Image segmentation of cucumbers based on color and textural features. Opt. Tech. 35:4 (2009), 529–531.
7. Li, W., Wang, K., Tan, Y.Z., Yang, Q.H., Gao, F., Yuan, T., Ren, Y.X., Hou, M.Y., Feng, Q.C., Study on the technology of cucumber harvesting robot in greenhouse. The Fourth Session of the National Advanced Manufacturing Equipment and Robotics, Harbin, China, 2008, 79–88.
8. Qi, L.Y., Gao, F., Tan, Y.Z., Yang, Q.H., Cucumber shape description based on elliptic Fourier descriptor. Trans. Chin. Soc. Agric. Mach., 42(8), 2011 164–167+142.
9. Qiao, B.M., Zhang, Y.D., Wei, G., Wang, S.H., Wu, L.N., Segmentation of cucumber color images. Comput. Inf. Technol. 17:6 (2009), 32–34.
10. Sule, T.A., Mohd, S.M.R., Enhancement of template-based method for overlapping rubber tree leaf identification. Comput. Electron. Agric. 122 (2016), 176–184.
11. Van Eck, J.W., Van Der Heijden, G.W.A.M., Polder, G., Accurate measurement of size and shape of cucumber fruits with image analysis. J. Agric. Eng. Res. 70:4 (1998), 335–343.
12. Van Henten, E.J., Van Tuij Hemming, B.A.J.J., Kornet, J.G., Bontsema, J., Van Os, E.A., Field test of an autonomous cucumber picking robot. Biosyst. Eng. 86:3 (2003), 305–313.
13. Van Henten, E.J., Van't Slot, D.A., Hol, C.W.J., Van Willigenburg, L.G., Optimal manipulator design for a cucumber harvesting robot. Comput. Electron. Agric. 65:2 (2009), 247–257.
14. Vassiliadis, S., Hakkennes, E.A., Wong, J.S.S.M., Pechanek, G.G., The sum absolute difference motion estimation accelerator. Euro micro Conference, Proceedings. 24th IEEE, vol. 2, 1998, 559–566.
15. Wang, H.Q., Ji, C.Y., Gu, B.X., Tian, G.Z., A simplified pulse-coupled neural network for cucumber image segmentation. 2010 International Conference on Computational and Information Sciences, Chengdu, China, 2010, 1053–1057.
16. Wang, H.Q., Ji, C.Y., Chen, K.J., Research on identification of cucumber stem and leaf based on spectrum analysis technology. Spectrosc. Spectral Anal. 31:10 (2011), 2834–2838.
17. Wang, H.Q., Ji, C.Y., Gu, B.X., An, Q., In-greenhouse cucumber recognition based on machine vision and least squares support vector machine. Trans. Chin. Soc. Agric. Mach., 43(3), 2012 163–167+180.
18. Xiang, R., Jiang, H.Y., Ying, Y.B., Recognition of clustered tomatoes based on binocular stereo vision. Comput. Electron. Agric. 106 (2014), 75–90.
19. Yang, Q.H., Qi, L.Y., Bao, G.J., Gao, F., Zhang, L.B., Cucumber image segmentation algorithm based on rough set theory. New Zealand J. Agric. Res. 50:5 (2007), 989–996.
20. Yang, Z.Y., Liu, F.Y., Wang, Y., Study of automatic navigation cucumber harvesting robot in greenhouse. J. Chin. Agric. Mechanization 34:6 (2013), 225–229.
21. Yuan, G.Y., Zhang, T.Z., Pattern recognition of cucumber image by Bayes sort discrimination model. J. Agric. Mechanization Res. 7 (2006), 150–153.
22. Yuan, T., Ji, C., Chen, Y., Li, W., Zhang, J.X., Greenhouse cucumber recognition based on spectral imaging technology. Trans. Chin. Soc. Agric. Mach. S1 (2011), 172–176.
23. Zhang, L.B., Yang, Q.H., Xun, Y., Chen, X., Ren, Y.X., Yuan, T., Tan, Y.Z., Li, W., Recognition of cucumber fruit in greenhouse using computer vision. New Zealand J. Agric. Res. 50:5 (2007), 1293–1298.
24. Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M., Shimizu, M., Disease detection of cercospora leaf spot in sugar beet by robust template matching. Comput. Electron. Agric. 108 (2014), 58–70.
25. Zhou, R., Kaneko, S., Tanaka, F., Kayamori, M., Shimizu, M., Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition. Comput. Electron. Agric. 116 (2015), 65–79.