Obstacle avoidance transplanting method based on Kinect visual processing
Keywords:
Kinect vision processing, oblique-type transplanting robot, path planning, obstacle avoidanceAbstract
In modern facility agriculture, to improve the quality and efficiency of transplanting, the application of transplanting robots based on visual processing is becoming more and more widespread. In order to reduce the damage to plants during the transplanting process and reduce the damage rate of plant stems, leaves and substrates, a transplanting method based on Kinect visual processing combined with an inclined transplanting manipulator was proposed. In the research, the Kinect visual processing was used to obtain and process the seedling height information and leaf edge information, and the working coordinate system of the transplanting manipulator was established and applied to plan the obstacle avoidance path. Combined with the oblique manipulator, the obstacle avoidance transplanting method was proposed. Through the structural design and force analysis of the seedling transplanting device, the key parameters that affect the transplanting quality were obtained, and the optimal transplanting performance parameters were obtained through experiments. In the experiment, with the aid of the Kinect vision processing system, the designed obstacle avoidance transplanting manipulator had a leaf damage degree of 4.70%, a stem bending rate of 16.67%, substrate integrity of 83.45% and a transplanting quality parameter of 87.36%. The time for a single seedling transplanting was (8.32±0.40) s. The experiment result proves that the obstacle avoidance transplanting method based on Kinect visual processing can effectively reduce the damage to seedlings when ensuring the transplanting efficiency. Keywords: Kinect vision processing, oblique-type transplanting robot, path planning, obstacle avoidance DOI: 10.25165/j.ijabe.20211405.6451 Citation: Jin X, Li R S, Ji J T, Yuan Y W, Li M Y. Obstacle avoidance transplanting method based on Kinect visual processing. Int J Agric & Biol Eng, 2021; 14(5): 72–78.References
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[2] Pu B S, Zheng H Y, Huang Y Y, Wu J C. Current status and suggestions of greenhouse agricultural facilities and equipment technology development in China. Jiangsu Agricultural Sciences, 2019; 47(14): 13–18. (in Chinese)
[3] Qin S C, Gu S, Wang Y W. The production system of hydroponic leafy vegetables mechanical large-scale in European. Agricultural Mechanization Research, 2017; 39(12): 264–269. (in Chinese)
[4] Tong J H. End-effector design, seedling information inspection and path planning for transplanting between vegetable seedling trays. PhD dissertation. Zhejiang: Zhejiang University, 2014; 123p.
[5] Jin X, Yuan Y W, Ji J T, Zhao K X, Li M Y, Chen K K. Design and implementation of anti-leakage planting system for transplanting machine based on fuzzy information. Computer and electronics in agriculture, 2020; 169: 105204. doi: 10.1016/j.compag.2019.105204.
[6] Yang S, Song J N, Wang J C, Wang C, Li Y L, Dong X Q. Design and kinematic simulation of chip seedling mechanism for an onion seedling transplanter. Journal of China Agricultural University, 2011; 16(5): 133–137. (in Chinese)
[7] Feng Q C, Wang X, Jiang K, Zhou J J, Zhang R, Ma W. Design and test of key parts on automatic transplanter for flower seedling. Transactions of the CSAE, 2013; 29(06): 21–27. (in Chinese)
[8] Feng Q C, Wang X. Development of research on automatic transplanter for tray seedlings. Journal of Agricultural Mechanization Research, 2013; 11: 250–252. (in Chinese)
[9] Yu Y F. Research on three translation parallel transplanting robot and its vision system. Master dissertation. Jiangsu: Jiangsu University, 2007; 89p.
[10] Sun G X. Automatic plug seedlings transplanting robot based on machine vision. Master dissertation. Jiangsu: Nanjing Agricultural University, 2009; 88p.
[11] Li B, Gu S, Chu Q, Lyu Y J, Hu J S, Xie Z J, et al. Design and experiment on manipulator for transplanting leaf vegetable seedlings cultivated by coco-peat. Transactions of the CSAE, 2017; 33(14): 18–24. (in Chinese)
[12] Jin X, Chen K K, Ji J T, Pang J, Du X W, Ma H. Intelligent vibration detection and control system of agricultural machinery engine. Measurement, 2019; 145: 503–510.
[13] Gao Y F, Chen D R, Gong J L. Edge image adaptive coding method. Navigation and Control, 2019; 18(1): 96–101.
[14] Zhang Z. Microsoft kinect sensor and its effect. IEEE Multimedia, 2012; 19(2): 4–10.
[15] Shen X X, Zhang H, Gao Z, Xu G P, Xue Y B, Zhang Z. Behavior recognition algorithm based on depth information and RGB images. Pattern Recognition and Artificial Intelligence, 2013; 26(8): 722–218.
[16] Pagliari D, Menna F, Roncella R, Pinto L. Kinect fusion improvement using depth camera calibration. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014; 6: 479–485.
[17] Werner D, Werner P, Al-Hamadi A. Quantitative analysis of surface reconstruction accuracy achievable with the TSDF representation. In: Proceedings of the International Conference on Computer Vision Systems, Chile, 2015; pp.167–176. doi: 10.1007/978-3-319-20904-3_16.
[18] Jin X, Li D Y, Ma H, Ji J T, Zhao K X, Pang J. Development of single row automatic transplanting device for potted vegetable seedlings. Int J Agric & Biol Eng, 2018; 11(3): 67–75.
[19] Inthiyaz S, Madhav B, Kishore P. Flower image segmentation with PCA fused colored covariance and gabor texture features based level sets. Ain Shams Engineering Journal, 2018; 9(4): 3277–3291.
[20] Ji W, Cheng F Y, Zhao D A, Tao Y, Ding S H, Lyu J D. Obstacle avoidance method of apple harvesting robot manipulator. Transactions of the CSAM, 2013; 44(11): 253–259. (in Chinese)
[21] Wang Y L, Liu Y H, Yu H. Design and simulation test research of automatic plug seedling manipulator. Jiangsu Agricultural Sciences, 2015; 6: 421–424.
[22] Gao G H, Feng T X, Li F. Working parameters optimization and experimental verification of inclined-inserting transplanting manipulator for plug seedlings. Transactions of the CSAE, 2015; 31(24): 24–30. (in Chinese)
[23] Feng T X. Optimization design and test research of plug seedlings end-effector in greenhouse. Mater dissertation. Beijing: Beijing University of Technology, 2016; 87p.
[24] Liu J D, Cao W B, Tian D Y, Ouyang Y N, Zhao H Z. Optimization experiment of transplanting actuator parameters based on mechanical property of seedling pot. Transactions of the CSAE, 2016; 32(16): 32–39. (in Chinese)
[25] Jiao X Q, Wang W. Research on the relevant practices of cultivating robust flower seedlings. Heilongjiang Science and Technology Information, 2011; 34: 272. (in Chinese)
[26] Tang Y X, Qu P, Liu X H, Xu H C, Gao J, Zhang D X, et al. Technical regulations for seedling cultivation of pepper plate suitable for mechanized transplantation. Jiangsu Agricultural Sciences, 2017; 45(23): 112–114. (in Chinese)
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Published
2021-10-13
How to Cite
Jin, X., Li, R., Ji, J., Yuan, Y., & Li, M. (2021). Obstacle avoidance transplanting method based on Kinect visual processing. International Journal of Agricultural and Biological Engineering, 14(5), 72–78. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6451
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Power and Machinery Systems
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