Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals

Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm (“WeedFinder”) estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red–green–blue images. The ability of “WeedFinder” to predict ‘spray’/‘no spray’ decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, “WeedFinder” gave correct spray decisions in 65–85% of the test images. With discriminant analysis, corresponding mean rates were 84–90%. Future versions of “WeedFinder” must be more accurate and accommodate weed species recognition. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals

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Publisher
Springer US
Copyright
Copyright © 2008 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-008-9083-z
Publisher site
See Article on Publisher Site

Abstract

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm (“WeedFinder”) estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red–green–blue images. The ability of “WeedFinder” to predict ‘spray’/‘no spray’ decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, “WeedFinder” gave correct spray decisions in 65–85% of the test images. With discriminant analysis, corresponding mean rates were 84–90%. Future versions of “WeedFinder” must be more accurate and accommodate weed species recognition.

Journal

Precision AgricultureSpringer Journals

Published: Sep 27, 2008

References

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