Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/35512
標題: 蝴蝶蘭組織培養苗自動夾持作業之研究
Study on Automatic Grasping Operation for Phalaenopsis Tissue Culture Plantlets
作者: Huang, Ying-Jen
黃膺任
關鍵字: Phalaenopsis plantlet
蝴蝶蘭組織培養苗
Machine vision
Feature selection
Robot
特徵選擇
機器視覺
機器手臂
出版社: 生物產業機電工程學系所
引用: 1. Astrand, B. and A. Baerveldt. 2003. A mobile robot for mechanical weed control. International Sugar Journal Vol. 105. No.1250. 2. Bouguet, J.Y. 2006. Camera Calibration Toolbox for Matlab: [on-line] Available: http://www.vision.caltech.edu/bouguetj/calib_doc/ 3. Brown, F. R. 1992. Robotics and image analysis applied to micropropagation. Transplant Production Systems 282-296. Kluwer Academic Publishers. 4. Chaisattapagon, C. and N. Zhang. 1992. Identifying effective criteria for weed detection using machine visvion. ASAE Paper 92-3576. St. Joseph, Mich.: ASAE 5. Chen, C. C. 1998. Development of the automation production of tissue culture plantlets for Phalaenopsis. Personal Communication. (in Chinese) 6. Cheng, S. F. and T. T. Lin. 1997.Growth measurement and modeling of cabbage seedlings (I) implementation of the automatic measurement system. Journal of Agricultural Machinery 6(4):69-82. (in Chinese) 7. Chien, D. W. 1999. A robotic system for transplanting Phalaenopsis tissue culture plantlets. Master thesis. Taichung, Taiwan: National Chung Hsing University, Department of Bio-industrial Mechatronics Engineering. (in Chinese) 8. Cooper, P. A. and J. E. Grant. 1992. Development of prototype automated cutting and placing system for tissue culture multiplication. Combined Proc. Int'l. Plant Propagators' Soc.42: 209-212. 9. Deleplanque, H., P. Bonnet, and J. G. Postaire. 1985. An intelligent robotic system for in vitro plantlet production. In Proc. 5th Int'l. Conf. Robot Vision and Sensory Controls, 205-214. Kempston, U.K.: IFS Ltd. 10. Deriche, R. and O. Faugeras. 1990. 2D Curve Matching Using High Curvature Points: Application to Stereo Vision. Proceedings of the 10th International Conference on Pattern Recognition, Vol. 1. pp. 240-242. 11. Ferri, F., P. Pudil, M. Hatef, and J. Kittler. 1994. Comparative Study of Techniques for Large Scale Feature Selection. Pattern Recognition in Practice IV. Elsevier Science B.V. pp. 403-413. 12. Franz, E., M. R. Gebhardt, and K. B. Unklesbay. 1991a. Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Transactions of the ASAE 24(2):672-681. 13. Franz, E., M. R. Gebhardt, and K. B. Unklesbay. 1991b. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Transactions of the ASAE 24(2): 682-687. 14. Franz, E., M. R. Gebhardt, and K. B. Unklesbay. 1995. Algorithms for extracting leaf boundary information from digital images of plant foliage. Transactions of the ASAE 28(2):625-622. 15. Fusiello, A., V. Roberto, and E. Trucco. 1997, Experiments with a new Area-Based Stereo Algorithm. Proceedings of ICIAP'97, pp. 669-676. 16. Gonzalez, R. C. and R.E. Woods. 2002. Digital Image Processing. Prentice-Hall, Inc. 17. Grimson, W. E. L., 1985. Computational experiments with a feature based stereo algorithm. PAMI 7 (1), 17-34. 18. Guyer, D. E., G. E. Miles, L. D. Gaulttney, and M. M. Schreiber. 1993. Application of machine vision to shape analysis in leaf and plant identification. Transactions of the ASAE 26(1):162-171. 19. Han, K. P., T. M. Bae, and Y. H. Ha. 2000. Hybrid stereo matching with a new relaxation scheme of preserving disparity discontinuity. Pattern Recognition 33: 767-785. 20. Hemayed, E. E., M. S. Brown, A. A. Farag, and W. B. Seales. 1999. Cooperative stereo: combining edge- and area-based stereo. Proceedings of IEEE Conference on Aerospac, Vol. 3, pp. 421-428. 21. Hoff, W. and N. Ahuja. 1989, Surface from stereo: integrating feature matching, disparity estimation, and contour detection, IEEE Trans. Pattern Anal. Mach. Intell. 11, 121-136. 22. Huang, K. Y. and T. C. Lin. 2001. Development of a sorting system for Phalaenopsis seedlings. Journal of Agricultural Machinery 10(4):85-98. (in Chinese) 23. Humphries, S. and W. Simonton. 1993. Identification of plant parts using color and geometric image data. Transactions of the ASAE 36(5):1493-1500. 24. Jain, A.K. and D. Zongker. 1997. Feature selection: evaluation, application and small sample performance. IEEE Trans. Pattern Analysis and Machine Intelligence, vol.19, No. 2. 153-158. 25. Jain, R., R. Kasturi ,and B. G. Schunck. 1995. Machine Vision. McGraw-Hill, Inc. 26. Kozai, T., K. C. Ting, and J. Aitken-Christie. 1991. Consideration for automation of micropropagation system. In Automated Agriculture for the 21st Century, 503-517. 27. Kuo, Y. F., T. T. Lin and C. J. Wang. 2004. Geometric modeling of plant leaves aided with image processing techniques. Journal of Agricultural Machinery 13(2):1-17. (in Chinese) 28. Laganie`re, R. and R. Elias. 2004. The detection of junction features in images. In: Proceedings of ICASSP'04, vol. III, pp. 573-576. based on a.ne invariant regions. IJCV 59 (1), 61-85. 29. Lee, C. H and N. Lee. 1991. Characteristics of morphology and anatomy in root and leaf of Phalaenopsis amabilis. J. Chinese Soc. Hort. Sci. 37(4):237-248. (in Chinese) 30. Liu, J., and M. R. Paulsen. 1997. Corn whiteness measurement and classification using machine vision. ASAE Paper No. 97-3045. St. Joseph, Mich.: ASAE. 31. Majumdar, S., D. S. Jayas, and N. R. Bulley. 1997. Classification of bulk samples of cereal grains using machine vision. ASAE Paper No. 97-3105. St. Joseph, Mich.: ASAE. 32. McFarlane, N. J. B. 1993. Image-guidance for robotic harvesting of micropropagated plants. Computers and Electronics in Agriculture. 8:42-56. 33. Meyer, G. E., J. C. Neto, and D. D. Jones. 2004. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture 42:161-180. 34. Okamoto, T. and O. Kitani, 1989. Studies on robotics for biotechnological operations (Part1). On callus handling robot. J. of Japanese Society of Agricultural Machinery, 51(5), 37-45. 35. Okamoto, T. and O. Kitani, 1990. Studies on robotics for biotechnological operations (Part2). Intelligent robot for transplanting calluses in subculture. J. of Japanese Society of Agricultural Machinery, 52(5), 79-85. 36. Okamoto, T. and O. Kitani, 1991. Studies on robotics for biotechnological operations (Part1). Fuzzy control of robot finger set driven by shape memory alloy actuators. J. of Japanese Society of Agricultural Machinery, 53(5), 85-91. 37. Okamoto, T., C. S. Zhao, Y. Miyama, T. Torii, and K. Imou. 1998. Robotic sugarcane seedling propagation system in tissue culture. J. of Japanese Society of Agricultural Machinery, 60(6), 71-77. 38. Pudil, P., J. Novovicova, and J. Kittler. 1994. Floating search methods in feature selection. Pattern Recognition Letters. 15(11):1119-1125. 39. Rao, S. S. 1996. Engineering Optimization Theory and Practice, 3rd ed. Wiley-Interscience, New York. 40. Schaufler, D. H. and P. N. Walker. 1994. Micropropagation of sugarcane between parallel plates. Transactions of the ASAE 27(4):1225-1220. 41. Schaufler, D. H. and P. N. Walker. 1995. Micropropagation of sugarcane shoot identification using machine vision. Transactions of the ASAE 28(6):1919-1925. 42. Shearer, S. A. and R. G. Holmes. 1990. Plant identification using color co-occurrence matrices. Transactions o the ASAE 33(6):2037-2044. 43. Shimizu, M. and M. Okutomi. 2001. Precise sub-pixel estimation on area-based matching. In: Proceedings of ICCV'01, vol. 1, pp. 90-97. 44. Simonton, W. and D. Graham. 1996. Bayesian and fuzzy logic classification for plant structure analysis. Applied Engineering in Agriculture 12(1):89-97. 45. Sobey, P.J., B. Harter, and A. Hinsch. 1997. Automated micro-propagation of plant material. Fourth Annual Conference on Mechatronics and Machine Vision in Practice. pp. 60-65. 46. Song, D. Ma. 1993. Conics-based stereo, motion estimation, and pose determination. Int. J. Computer Vision 10 (1):7-25. 47. Suh, S. R. and G. E. Miles. 1988. Measurement of morphological properties of tree seedlings using machine vision and image processing. ASAE Paper No. 881542. 48. Takayama, S., B. Swedlund, and Y. Miwa. 1991. Automated propagation of microbulbs of lilies. In Cell Culture and Somatic Cell Genetics of Plants, Vol. 8: 112-131. 49. Tillett, R. D. 1990. Vision-guided planting of dissected microplants. J. Agric. Engng. Res. 46(2): 197-205. 50. Tsai, R. Y. 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robotics and Automation. Vol. 3, No.4, pp. 323-344. 51. Wang, Z., P. H. Heinemann, P. N. Walker, C.T. Morrow, H. J. Sommer, and C. Heuser. 1996. Vision-guided separation of micropropagated sugarcane shoots. ASAE Paper No. 96-2096. 52. Wang, Z., P. H. Heinemann, H. J. Sommer, P. N. walker, C.T. Morrow, and C. Heuser. 1998. Identification and separation of micropropagated sugarcane shoots based on the Hough transform. Transactions of the ASAE 41(5):1525-1541. 53. Wang, Z., P. H. Heinemann, P. N. Walker, and C. Heuser. 1999. Automated micropropagated sugarcane shoot separation by machine vision. Transactions of the ASAE, 42(1), 247-254. 54. Watake, H. 1991. Micropropagation robot. Proceedings of 2nd National Symposium on Recent Development of Transplant Production Systems. Organizing Committee of International Symposium on Transplant Production Systems, Tokyo, pp. 67-73. 55. Weng, J. Y., P. Cohen, and M. Herniou. 1992. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, No.10, pp. 965-980. 56. Woebbecke, D. M., G. E. Meyer, K. Von Bargen and D. A. Mortensen. 1995. Shape features for identifying young weeds using image analysis. Transactions of the ASAE 28(1): 259-270. 57. Yamashita, T., T. Murase, and Y. Miwa. 1991. Automation of plantlets dissection in tissue culture. Proceedings of JSPE Autumn Meeting. pp. 252-254. 58. Zhang, N. and C. Chaisattapagon. 1995. Effective criteria for weed identification in wheat fields using machine vision. Transactions of the ASAE 28(2): 965-974. 59. Zhang, Z. Y. 2000. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.11, pp. 1330-1334.
摘要: In the manual transplanting of Phalaenopsis plantlets, the plantlet is usually grasped at the root or the stem since the leaf is fragile and is easily damaged by the gripper. To mimic the manual transplanting operation, this study developed an image-processing algorithm to segment and classify the components of Phalaenopsis tissue culture plantlets (PTCPs) and to determine a suitable grasping location to carry out an automatic grasping process. In the proposed approach, the nodes of the plantlet's skeleton are automatically located and used to generate cutting lines with which to separate the plantlet into its constituent leaves and roots. The sequential forward floating selection (SFFS) algorithm was employed to establish the optimal combination of color and shape features. A Bayes classifier based on the optimal combination was then applied to classify the individual components of the plantlet as either leaf or root segments. The root segment with the highest Bayes decision value was selected, and the midpoint of its skeleton was then specified as a suitable grasping point for the automatic grasping operation. Implementing the optimal set of features, the proposed classifier achieved a successful rate of 94.9% in identifying suitable grasping points on complete PTCP plantlets. An automatic grasping mechanism was constructed in which the grasping point selection scheme was integrated with a binocular stereo vision system and a robotic gripper. It is shown that the automatic grasping system has a success rate of 78.2% in grasping the plantlets in an appropriate position.
在蝴蝶蘭組織培苗的人工移植作業中,由於葉子部較脆弱而易受夾爪所損傷,因此移植時通常選擇根或莖的部位進行夾持。為了模擬這樣的人工移植方式,本研究發展了一套影像處理演算法,可對蝴蝶蘭苗的影像進行苗體元件分割與分類,並從中選定一合適的夾持位置來執行夾持作業。 本文利用苗影像的骨架分叉點來產生分割線,將苗體分割成一段一段的莖葉元件,然後使用SFFS (Sequential Forward Floating Selection) 特徵選擇演算法就擷取的顏色與形狀特徵參數中,選出一組辨識率最佳的特徵組合。再根據最佳的特徵組合建立一個Bayes 分類器,將被分割的元件區分為葉或根兩種類別。然後在被分類為根的元件選定具有最高之根類Bayes決策值者作為夾持目標,並取該元件之骨架中點作為自動夾持作業選定的夾持點。 利用最佳特徵模式所建立的分類器對整株苗進行夾持點辨識試驗,結果顯示夾持點被成功的選定在苗的根部之比率為94.9%。再利用此夾持點辨識演算法結合雙眼立體視覺與夾持機構所建立之自動夾持系統,對苗執行自動夾持作業,實驗結果顯示,此自動夾持系統成功地將苗夾持在適當位置的比率為78.2%。
URI: http://hdl.handle.net/11455/35512
其他識別: U0005-1607200809451000
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1607200809451000
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