Accurate Identification and Location of Corn Rhizome Based on Faster R-CNN
Abstract
In order to identify and locate the maize rhizomes accurately, a maize rhizome detection network based on the migration learning method was established. The function of human eye recognition to identify and locate the rhizomes of the corn from a complex field environment was simulated, which achieved the function of crawler heat fog machine walking along the corn line. Field image of corn was collected by crawler self-propelled hot fogging machine, construction of a precise identification and location model of corn rhizome based on convolutional neural network, and the “DOG Pyramid” algorithm was used to extract maize rhizome as the target from the images, which constituted the training sample database. Through training network, the single maize rhizome was precisely identified firstly, and then were accurately identified and located in the environment of corn crop. The path tracking was obtained by east square fitting algorithm based on the identified maize rhizome location, and the sliding mode track tracking algorithm was used to control the double differential drive motor of the caterpillar chassis to realize the path tracking. The test result showed that the corn root recognition method can identify and locate the maize rhizomes more accurately, the correct rate of identification and location of corn rhizome reached 91.4%, but the traditional image processing method can only reach 67.3%. It can be seen that the method of identifying maize rhizomes proposed had better positioning accuracy, which can better plan the corn field path accurately. The research results provided the key technical support for the crawler self-propelled hot fogging machine self walking along the intercropping of corn.
Keywords: hot fogging machine, maize rhizome, migration learning, identification and location, path planning
Download Full Text:
PDFReferences
LI Zhigang,FU Zetian,LI Liqin. Advance in agricultural plant protection technology based on machine vision [J ]. Transactions of the Chinese Society for Agricultural Machinery, 2005,36(8) ; 143 - 146. ( in Chinese)
D1A0 Zhihua, ZHAO Mingzhen, SONG Yinmao,et al. Crop line recognition algorithm and realization in precision pesticide system based on machine vision [ J ]. Transactions of the CSAE ,2015 ,31 ( 7 ) ; 47 -52. (in Chinese)
http: // www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag = l&file_no = 20170205&journal_id = jcsam. DOI: 10. 6041/j. issn. 1000-1298. 2017.02.005. SONG Yu, LIU Yongbo, LIU Lu, et al. Extraction method of navigation baseline of corn roots based on machine vision [J/OL]. Transactions of the Chinese Society for Agricultural Machinery ,2017 ,48 ( 2 ) :38 -44. ( in Chinese)
http://www.j- csam. оrg/jcsam/ch/reader/view_abstract. aspx? flag = l&file_no = 20170903&journal_id = jcsam. DOI: 10. 6041/j. issn. 1000- 1298. 2012.07.034.FENG Juan, LIU Gang, SI Yongsheng,et al. Algorithm based on image processing technolog)- to generate navigation directrix in orchard [ J/OL]. Transactions of the Chinese Society for Agricultural Machinery, 2012 ,43 ( 7) : 185 — 189,184. (in Chinese)
JIN Hailong,YU Qingcang, ZHOU Zhiyu,et al. Extraction of center line of rice seedling row based on Meanshift and Hough transform[ J ]. Journal of Zhejiang Sci-Tech University ,2015 ,33 ( 3 ) ;405 -409. ( in Chinese)
LAKSHMI N A, JEEVA J B. A computer aided diagnosis for detection and classification of lung nodules [ С ] // IEEE International Conference on Intelligent Systems and Control, 2015:1 -5.
SUN Haoze, CHANG Tianqing, W ANG Quandong, et al. Image detection method for tank and armored targets based on hierarchical multi-scale convolution feature extraction [J ]. Acta Armamentarii, 2017, 38(9) : 1681 - 1691. (in Chinese)
DAI Chenka, LI Yi. Aeroplane detection in static aerodrome based on FASTER RCNN and multi-part model [ J ]. Journal of Computer Applications, 2017 ,37( Supp. 2 ) :85 -88. ( in Chinese)
DOBHAL T, SHITOLE V, THOMAS G, et al. Human activity recognition using binary motion image and deep learning [J ]. Procedia Computer Science, 2015, 58; 178 - 185.
SINGH R, ОМ H. Newborn face recognition using deep convolutional neural network [J ]. Multimedia Tools & Applications,2017, 76( 18) ; 19005 - 19015.
RONAO С A, CHO S B. Human activity recognition with smartphone sensors using deep learning neural networks[J]. ExpertSystems with Applications, 2016, 59; 235 -244.
WANG Zhongmin, CAO Hongjiang, FAN Lin. Method on human activity recognition based on convolutional neural networks [J ] . Computer Science, 2016, 43 (Supp. 2) : 56 -58. (in Chinese)
http: // www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? file_no = 20180506&flag = 1. DOI: 10. 6041/j. issn. 1000-1298. 2018. 05.006. LIU Deying,WANG Jialiang,LIN Xiangze, et al. Automatic identification method for sogatella furcifera based on convolutional neural network [J/OL] . Transactions of the Chinese Society for Agricultural Machinery ,2018 ,49( 5 ) ;5 1 -56. ( in Chinese)
SUN Jun, TAN Wenjun, MAO Hanping, et. al. Recognition of multiple plant leaf diseases based on improved convolutional neural network [J ]. Transactions of the CSAE, 2017,33 ( 19): 209 —215. (in Chinese)
http: // www. j-csam. org/jcsam/ch/reader/view_abstract, aspx? flag = l&file_no = 20170903&journal_id = jcsam. DOI: 10. 6041/j. issn. 1000-1298. 2017.09.003. TAN Wenxue, ZHAO Chunjiang, WU Huarui, et al. A deep learning network for recognizing fruit pathologic images based on flexible momentum [J/OL ]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46( 1 ) :20 -25. ( in Chinese)
FU Longsheng, FENG Yali, ELKAMIL T, et al. Image recognition method of multi-cluster kiwifruit in field based on convolutional neural networks [ J ]. Transactions of the CSAE, 2018, 34(2) : 205 —211. (in Chinese)
Refbacks
- There are currently no refbacks.