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Design of Sugarcane Orientation System Based on Machine Vision

Published: 04 January 2021 Publication History

Abstract

This subject is based on the pre-cut sugarcane planting machine, combined with the agronomic requirements of the sugarcane buds to be planted in the horizontal direction during the sugarcane planting process, to study the sugarcane directional planting system. The cut cane seed segments are placed in the sugarcane planting machine seed box. The initial orientation of the cane buds on the cane seeds is different. Therefore, the combination of machine vision and mechanical structure is used to identify and adjust the direction of the cane buds on the cane seeds. An important step in the planting plan. Adjusting the centroid of the cane bud to the horizontal direction is the key point of the machine vision recognition scheme. Therefore, it is necessary to formulate a reasonable visual recognition scheme and evaluation index. By comparing different cane bud visual detection algorithms, it is found that the convolutional neural network is more suitable for the cane bud. The recognition and positioning of the sugarcane has a success rate of 90%, which provides a research foundation for the intelligent and precise planting of sugarcane.

References

[1]
Zhan Pengju. The development status and countermeasures of Guangxi sugar industry under the global background [D]. Guangxi University, 2013.
[2]
Yang Rongfen. Analysis and application of sugarcane planting techniques[J]. Beijing Agriculture, 2014(18): 46--47.
[3]
Wu Yipeng. The mechanization of sugarcane production starts from planting [J]. Agricultural Machinery Market, 2013, 3:8.
[4]
Shi Changyou. Research on sugarcane stem node recognition technology based on computer vision[D]. Northwest Sci-tech University of Agriculture and Forestry, 2019...
[5]
Yu Yaxin, Lin Jiahui, Zhao Yun, et al. Simulation and experiment of directional sequencing of rice seeds based on uniaxial symmetry[J]. Transactions of the Chinese Society of Agricultural Machinery, 2013, 44(10): 62--67.
[6]
Wang Yingbiao, Zhao Xueguan, Xu Liming, et al. Directional sorting and conveying technology of corn seeds based on electromagnetic vibration[J]. Transactions of the Chinese Society of Agricultural Machinery, 2015, 46 (1): 79--88.
[7]
Yang Yanli, Gu Song, Li Kai, Liu Kai, Zhang Qing, Zhong Luxiang, Jia Dongdong, Liu Xiaoliang. Parameters optimization experiment of directional precision seeding device for large seeds[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(13): 15- twenty two
[8]
Moshashai K, Almasi M, Minaei S, et al.Identification of sugarcane nodes using image processing and machine visiontechnology. International Journal of Agricultural Research. 2008
[9]
Lu Shangping. Research on Sugarcane Stem Node Recognition and Sugarcane Bud Detection Based on Machine Vision [D]. Wuhan: Huazhong Agricultural University, 2011
[10]
Qiao Xi. Research on Sugarcane Seed Stem Cutting and Bud Prevention System Based on Computer Vision [D]. Nanning: Guangxi University, 2013.
[11]
Zhang Weizheng, Zhang Weiwei, Zhang Huanlong, Chen Qiqiang, Ding Chenchen. Research on recognition and location of sugarcane stem nodes based on hyperspectral imaging technology[J]. Journal of Light Industry, 2017, 32(05): 95--102.
[12]
Huang Yiqi, Huang Tisen, Huang Meizhang, Yin Kai, Wang Xiaobo. Sugarcane stem node recognition based on local mean [J]. Chinese Journal of Agricultural Machinery Chemistry, 2017, 38(02): 76--80.

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  1. Design of Sugarcane Orientation System Based on Machine Vision

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    ISBDAI '20: Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence
    April 2020
    640 pages
    ISBN:9781450376457
    DOI:10.1145/3436286
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 January 2021

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    Author Tags

    1. Sugarcane
    2. directional planting
    3. machine vision

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