Design and Experiment of Double Disc Cotton Topping Device Based on Machine Vision
Abstract
In order to reduce the labor intensity of manual topping, the environmental pollution of chemical topping and the over-topping of the traditional “one-size-fits-all” topping mechanism, a double disc clamping topping mechanism was designed by analyzing the manual topping process. Based on machine vision, a single-line prototype of the topping device was designed to realize the automatic control of the whole process of cotton topping. It was mainly composed of topping mechanism, visual inspection mechanism, motion mechanism, cotton top recognition and control system. Based on cotton field research, structural calculation and pre-test, the overall structure and key component dimensions of the topping device were determined. Combined with the research basic and practical application of visual recognition, the YOLO v3 algorithm was selected to build the cotton top recognition and control system, realize the recognition and positioning of cotton top, and complete the motion control of the topping mechanism. Taking cotton in the topping stage as the research object, the cotton top recognition test, the topping mechanism performance test and the field comprehensive test were carried out. The results showed that the average recognition rate of cotton top recognition test was 93%;the average topping rate of the performance test of the topping mechanism was 94.67%;the average recognition rate and average topping rate in the field test was 85.33%, and the average topping rate was 78.22%. The research result can provide reference for the precise and intelligent research of cotton topping.
Keywords: cotton topping, double disc, machine vision, YOLO v3, recognition and positioning, motion control
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GUI Yannan, HAN Wenting, NIE Zhiyong, et al. Effects of three topping methods on cotton plant type structure and yield under different densities [ J ] . Xinjiang Agricultural Sciences, 2018, 55 ( 11); 1968 — 1976. (in Chinese)
WANG Gang, HAN Huanyong, WANG Xuwen, et al. Effect of different chemical topping agents on the population quality of cotton in Xinjiang [J] . China Cotton, 2021, 48(10) : 21 -24. (in Chinese)
CHEN Changlin, XIE Qing, ZHANG Aimin, et al. Analysis on production technology route of mechanized topping of cotton in the Yellow River Basin of China [J ]. Journal of Chinese Agricultural Mechanization, 2020, 41(12); 12-14, 167. (in Chinese)
HE Lei, LIU Xiangxin, ZHOU Yali, et al. Vertical lifting single profiling cotton topping machine [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2013, 44(Supp.2) ; 62 -67. (in Chinese)
PENG Qiangji, KANG Jianming, SONG Yumin, et al. Design of 3MDZ — 4 self-propelled cotton topping spraying combined machine [J] . Transactions of the CSAE, 2019, 35(14) ; 30 -38. (in Chinese)
PENG Qiangji, JIAN Shichun, SONG Heping, et al. Development of 3MDZJ — 1 type power driven intelligent precision cotton topping machine [j]. Journal of Agricultural Mechanization Research, 2016, 38( 12) ; 117 -121. (in Chinese)
LUO Xin, HU Bin, WANG Weixin, et al. 3MDZK — 12 type group control single row profiling cotton topping machine [ J ]. Journal of Agricultural Mechanization Research, 2008, 30( 11): 136 - 138. (in Chinese)
JIANG Yongxin, CHEN Fa, W ANG Chunyao, et al. 3FDD — 6 types queen flies drum-tvpe cotton topping machine [ J ]. Chinese Agricultural Mechanization, 2012, 33( 1 ) : 126 -128. (in Chinese)
ZHANG Zhenqian, LI Shichao, LI Chenyang, et al. Navigation path detection method for a banana orchard inspection robot based on binocular vision [J] . Transactions of the CSAE, 2021 , 37(21 ) : 9 - 15. (in Chinese)
ZHAI Changyuan, FU Hao, ZHENG Kang, et al. Establishment and experimental verification of deep learning model for on¬line recognition of field cabbage[ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(4) ; 293 - 303. (in Chinese)
HAN Changjie, ZHENG Kang, ZHAO Xueguan, et al. Design and experiment of row identification and row-oriented spray control system for field cabbage crops[ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(6) ; 89 - 101. (in Chinese)
LIU Haitao, YI Lili, LAN Yubin, et al. Research progress in the application of machine vision in the field of intelligent topping [J ]. Journal of Chinese Agricultural Mechanization, 2021 , 42(6) ; 159 - 165. (in Chinese)
WANG Xiangyou, LI Yanxing, YANG Zhenyu, et al. Detection method of clods and stones from impurifled potatoes based on improved YOLO v4 algorithm [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2021 , 52(8) ; 241 -247, 262. ( in Chinese)
CAI Shuping, PAN Wenhao, LIU Hui, et al. Orchard obstacle detection based on D2 — YOLO deblurring recognition network [J ]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(2) ; 284 -292. (in Chinese)
ZHANG Zhivuan, LUO Mingvi, GUO Shuxin, et al. Cherry fruit detection method in natural scene based on improved YOLO v5 [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(Supp. 1 ) ; 232 -240. (in Chinese)
CHEN Bin, ZHANG Man, XU Hongzhen, et al. Farmland obstacle detection in panoramic image based on improved YOLO v3 — tiny [ J ] - Transactions of the Chinese Society for Agricultural Machinery ,2021 , 52(Supp. ) : 58 -65. (in Chinese)
ZHANG Fu, CHEN Zijun, BAO Ruofei, et al. Recognition of dense cherry tomatoes based on improved YOLO v4 - LITE lightweight neural network[J]. Transactions of the CSAE, 2021 , 37( 16) : 270 -278. (in Chinese)
SUN Xiang, WU Huarui, ZHU Huaji, et al. Research on detection and location of cotton main stem growth point via LW— YOLO v3 model[ J ]. Journal of Hebei Agricultural University, 2021 , 44(6) : 106 - 115. (in Chinese)
LU Wei, ZOU Mingxuan, SHI Haonan, et al. Technology of visual identification — measuring — location for brown mushroom picking based on YOLO v5 — TL[ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53 (11); 341 - 348. ( in Chinese)
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once; unified, real-time object detection [ С ] // Computer Vision and Pattern Recognition. IEEE, 2016: 779 -788.
REDMON J, FARHADI A. YOLO v3: an incremental improvement [J]. arXiv preprint arXiv; 1804.02767, 2018.
CHAI Yu, XU Jike. Target recognition and positioning system based on machine vision [ J ]. Computer Engineering and Design, 2019, 40( 12) : 3557 -3562. (in Chinese)
XIANG Rong, YING Yibin, JIANG Huanyu, et al. Localization of tomatoes based on binocular stereo vision [J ]. Transactions of the CSAE, 2012, 28(5) : 161-167. (in Chinese)
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