Study on Image Detection Method of Navigation Route for Cotton Harvester

Abstract:

Article Preview

This paper presents an image detection algorithm for navigation route of cotton harvester. Two cameras were respectively installed on the leftmost and rightmost picker unit, and images were captured during working process respectively. Firstly, the color characteristics among harvested field, un-harvested field, outside-field and the end of field were analyzed, then the target features of different fields was extracted using the color difference 3B-R-G. Secondly, candidate point group was determined by looking for the critical point of peak from the lowest trough point to un-harvested field and associating with the detection result of the anterior frame. Lastly, navigation line was obtained by using passing a Known Point Hough Transform (PKPHT). Results show that the navigation line detected using this algorithm can fit the boundary line and the edge of field accurately, the average processing time is56.10ms/f, and the algorithm can meet the actual production needs of cotton harvester.

Info:

Periodical:

Edited by:

Fangyin Cheng and Yan Ma

Pages:

219-224

DOI:

10.4028/www.scientific.net/AMM.246-247.219

Citation:

J. B. Li et al., "Study on Image Detection Method of Navigation Route for Cotton Harvester", Applied Mechanics and Materials, Vols. 246-247, pp. 219-224, 2013

Online since:

December 2012

Export:

Price:

$38.00

[1] Ollis M, Stentz A (1996) First result in vision-based crop line tracking. Proceedings of the 1996 IEEE Conference on Robotics and Automation(ICRA'96), Minneapolis, MN: 951-956.

DOI: 10.1109/robot.1996.503895

[2] Pilarski T, Happold M, Pangels H, etal (2002). The Demeter system for automated harvesting. Autonomous Robots, 13(1): 9-20.

[3] E.R. Benson, J.F. Reid, Q. Zhang (2003) Machine Vision-based Guidance System for Agricultural Grain Harvesters using Cut-edge Detection. Biosystems Engineering, 86 (4): 389-398.

DOI: 10.1016/j.biosystemseng.2003.07.002

[4] H.T. Søgaard, H.J. Olsen (2003) Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture, 38: 141-158.

DOI: 10.1016/s0168-1699(02)00140-0

[5] Zhou Jun, Ji Changying (2003) Multi-resolution Road Recognition for Vision Navigation. Transactions of the Chinese Society for Agricultural Machinery . 34(6): 120-123(in chinese).

[6] Wu Gang, Tan Yu , Zheng Yongjun, etal (2010) Walking Goalline Detection Based on Improved Hough Transform on Harvesting Robot, Transactions of the Chinese Society for Agricultural Machinery. 41(2): 176-179(in chinese).

[7] B. Chen, S. Tojo, K Watanabe (2003) Machine Vision Based Algorithmic Guiding System for Automatic Rice Transplanters, Applied Engineering in Agriculture . 19(1), 40 - 46.

DOI: 10.13031/2013.12726

[8] B. Chen, S. Tojo, K. Watanabe (2002): Detection Algorithm for Traveling Route in Paddy Field for Automated Managing Machines, Transaction of the ASAE 45(1): 239-246.

DOI: 10.13031/2013.7862

[9] B. Chen, S. Tojo, K. Watanabe (2003) Study on Machine Vision for Micro Weeding Robot in Paddy Field, Biosystems Engineering, 85(4): 393-404.

DOI: 10.1016/s1537-5110(03)00078-3

In order to see related information, you need to Login.