Path-finding by Image Processing in Agricultural Field Operations

Gerrish, John B.; Stockman, George C.; Mann, L.W.; Hu, Gongzhu

doi:10.4271/861455

Cited by 16 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anumber of image processing techniques have been investigated to find the guidance course (directrix) from row crop images. As examples, Gerrish et al (1985) concluded in their study that thresholded intensity images alone will not work in all cases and they showed that a combination of noise filtering, edge detection, thresholding and re-scaling was the most promising technique. Image analysis using the Hough transform to find crop rows were reported in several studies (Marchant & Brivot 1995;Marchant 1996).…”

Section: A07128; Online Publication Date 9 April 2008 Received and Acmentioning

confidence: 99%

A robust approach to obtain a guidance directrix for a vision‐based agricultural vehicle guidance system

Wei¹,

Liu²

2007

New Zealand Journal of Agricultural Research

View full text Add to dashboard Cite

Amachine vision algorithm is presented to obtain a guidance directrix for an automatic vehicle guidance system. The vision algorithm is based on a binary transform by adaptive threshold, acnode filtering and an improved Hough transform. The outputs were the equations of the lines representing the rows. The acnode filtering was used to eliminate image noise. The least squares method and the Hough transform are the common methods to check the lines. However, the least square method was sensitive to noise points and the traditional Hough transform is very slow. To resolve this problem, an improved Hough transform is proposed. The algorithm was successfully implemented in a vision-based automatic guidance system for an experimental car. Real-time processing experiments showed that the algorithm met real-time processing requirements.

show abstract

Section: A07128; Online Publication Date 9 April 2008 Received and Acmentioning

confidence: 99%

A robust approach to obtain a guidance directrix for a vision‐based agricultural vehicle guidance system

Wei¹,

Liu²

2007

New Zealand Journal of Agricultural Research

View full text Add to dashboard Cite

show abstract

“…However, two problems have prevented adoption of this mechanism: 1) they protrude from the vehicle and are thus nuisance at the ends of a field, and 2) errors cause a great deal of steering activity that often results in magnified errors on subsequent passes (Gerrish et al, 1986). Because they require contact with the crop edge, they are prone to wear.…”

Section: Mechanical Systemsmentioning

confidence: 99%

“…The advantage of image processing using computer-vision is 1) a look-ahead capability which may enable efficient open-loop steering corrections while avoiding the over-steering which troubles "near-sighted" or tactile systems, 2) no out-board rigging, and 3) a potential for adaptation to a number of field crops and operations (Gerrish et al, 1986). Reid et al (1985) investigated image sensing for determining guidance infor mation from row crop images.…”

Section: Photoelectric Systemsmentioning

confidence: 99%

Self-Tuning Controller for Farm Tractor Guidance

Noh¹,

Erbach²

1991

SAE Technical Paper Series

View full text Add to dashboard Cite

The most advanced technology has been used to photograph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be rioted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book.

show abstract

“…While their work was valuable in establishing that machine vision techniques could be applied to this task, there were some significant limitations: their system computes only straight line boundaries, requires a specialized digital signal processor, and has not been used to guide an actual vehicle. Gerrish et al [4] used a variety of edgedetection and template-matching techniques to pick out "work edges"; these were tested on actual field images (including alfalfa). Straight line boundaries were still assumed, however, and processing times required were on the order of 20 seconds using a 68000 processor.…”

Section: Introductionmentioning

confidence: 99%

Vision-based perception for an automated harvester

Ollis

Stentz²

Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World

View full text Add to dashboard Cite

This paper describes a vision-based perception system which has been used to guide an automated harvester cutting fields of alfalfa hay. The system tracks the boundary between cut and uncut crop; indicates when the end of a crop row has been reached; and identifies obstacles in the harvester's path. The system adapts to local variations in lighting and crop conditions, and explicitly models and removes noise due to shadow.Injield tests, the machine has successfully operated in four different locations, at sites in Pennsylvania, Kansas, and California. Using the vision system as the sole means of guidance, over 60 acres have been cut at speeds of up to 4.5 mph (typical human operating speeds range f m m 3-6 mph). Future work largely centers around combining vision and GPS based navigation techniques to produce a commercially viable product for use either as a navigation aid or for a completely autonomous system.

show abstract

Path-finding by Image Processing in Agricultural Field Operations

Cited by 16 publications

References 2 publications

A robust approach to obtain a guidance directrix for a vision‐based agricultural vehicle guidance system

A robust approach to obtain a guidance directrix for a vision‐based agricultural vehicle guidance system

Self-Tuning Controller for Farm Tractor Guidance

Vision-based perception for an automated harvester

Contact Info

Product

Resources

About