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Proceedings Paper

Image analysis for vision-based agricultural vehicle guidance
Author(s): Neil H. Brown; Hugh C. Wood; James N. Wilson
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Paper Abstract

As the sizes of today''s farms increase larger equipment is required to ensure the timeliness of field operations. The operation of this equipment requires a significant amount of concentration by the operator. As a result automatic guidance of agricultural equipment is a very promising method of increasing the farmer''s productivity. The objective of this research is to develop and evaluate real-time image processing techniques to extract guidance information from digitized video images of tilled and untilled soil and standing crop and stubble1. The guidance information consists of the vehicle''s heading and lateral offset errors. These errors can be used to supply the operator with a visual indication of the amount of overlap or missing occurring. Ultimately automatic steering of the vehicle will be possible. Still video images of tilled and untilled soil and standing crop and stubble were gathered in the field then digitized and stored on computer disk. The images were then analyzed using three image processing algorithms two of which were developed by the authors. The results indicate that the heading errors as predicted by the algorithms agreed with the visually estimated heading errors to within 2 on average. Agreement of 0. 1 m or less (on average) was observed between the offset errors as predicted by the algorithms and the visually estimated offset errors. The computation times of each algorithm do not currently meet real-time requirements however for

Paper Details

Date Published: 1 February 1991
PDF: 15 pages
Proc. SPIE 1379, Optics in Agriculture, (1 February 1991); doi: 10.1117/12.25075
Show Author Affiliations
Neil H. Brown, Univ. of Saskatchewan (Canada)
Hugh C. Wood, Univ. of Saskatchewan (Canada)
James N. Wilson, Univ. of Saskatchewan (Canada)

Published in SPIE Proceedings Vol. 1379:
Optics in Agriculture
James A. DeShazer; George E. Meyer, Editor(s)

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