Crop row detection based on machine vision

被引：0

作者：

Jiang, Guoquan ^{[1
]}

Ke, Xing ^{[1
]}

Du, Shangfeng ^{[1
]}

Zhang, Man ^{[1
,2
]}

Chen, Jiao ^{[1
]}

机构：

[1] College of Information and Electrical Engineering, China Agricultural University

[2] Key Laboratory of Modern Precision Agriculture System Integration Research, China Agricultural University

来源：

Guangxue Xuebao/Acta Optica Sinica | 2009年 / 29卷 / 04期

关键词：

Hough transform; Line detection; Machine vision; Randomized algorithm;

D O I：

10.3788/AOS20092904.1015

中图分类号：

学科分类号：

摘要：

A crop row detection method based on machine vision was put forward to detect crop rows from farmland features images fast and effectively. In the process of image pre-processing, a new algorithm of the central line detection was proposed instead of the traditional vertical projection, and then a novel line detection method based on randomized algorithm was presented. Firstly, two different points are selected randomly from data space composed of locating points which determine a candidate line. Secondly, under the given distance tolerance, a strip region along the line direction is got, and the number of locating points in the region was accumulated. Lastly, the threshold rules are applied to determining whether the candidate line is the desired one. Test results show that the method can accurately find the crop rows under different light conditions and different growth stages. The image processing time is about 120 ms. Compared with Hough Transform (HT) and Randomized Hough Transform (RHT), the proposed algorithm has the advantages of smaller size of computer memory, shorter computational time and good robustness.

引用

页码：1015 / 1020

页数：5

共 13 条

[1]

Wilson J.N., Guidance of agricultural vehicles a historical perspective, Computers and Electronics in Agriculture, 25, 1-2, pp. 3-9, (2000)

[2]

Yang W., Li T., Jia H., Simulation and experiment of machine vision guidance of agriculture vehicles, Transactions of the CSAE, 20, 1, pp. 160-165, (2004)

[3]

Rovira-Ma's F., Zhang Q., Reid J.F., Et al., Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle, J. Automobile Engineering, 219, 88, pp. 999-1010, (2005)

[4]

Bakker T., Wouters H., van Asselt K., Et al., A vision based row detection system for sugar beet, Computers and Electronics in Agriculture, 60, 1, pp. 87-95, (2008)

[5]

Reid J.F., Zhang Q., Noguchi N., Et al., Agricultural automatic guidance research in North America, Computers and Electronics in Agriculture, 25, 3, pp. 155-167, (2000)

[6]

Zhao Y., Chen B., Wang S., Et al., Fast detection of furrows based on machine vision on autonomous mobile robot, Transactions of the Chinese Society of Agridus Madinery, 37, 4, pp. 83-86, (2006)

[7]

Shang F., Wang F., Tian D., Et al., A method for circle detection based on right triangles inscribed in a circle, Acta Optica Sinica, 28, 4, pp. 739-743, (2008)

[8]

Kultanen P., Xu L., Oja E., Randomized Hough transform, Proceedings of IEEE Pattern Recognition, pp. 631-635, (1990)

[9]

Sun Y., Zhang S., Li W., Guidance lane detection for pesticide spraying robot in cotton fields, J. Tsinghua University (Sci & Tech), 47, 2, pp. 206-209, (2007)

[10]

Yao Y., Li Y., Liu B., Et al., Analysis of real photonic crystal fibers by finite-difference frequency-domain method combined with digital image processing, Acta Optica Sinica, 28, 7, pp. 1384-1389, (2008)

← 1 2 →