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Proceedings Paper

Hyperspectral image sensor for weed-selective spraying
Author(s): Filip Feyaerts; P. Pollet; Luc J. Van Gool; Patrick Wambacq
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Paper Abstract

Recognizing, online, cops and weeds enables to reduce the use of chemicals in agriculture. First, a sensor and classifier is proposed to measure and classify, online, the plant reflectance. However, as plant reflectance varies with unknown field dependent plant stress factors, the classifier must be trained on each field separately in order to recognize crop and weeds accurately on that field. Collecting the samples manually requires user-knowledge and time and is therefore economically not feasible. The posed tree-based cluster algorithm enables to automatically collect and label the necessary set of training samples for crops that are planted in rows, thus eliminating every user- interaction and user-knowledge. The classifier, trained with the automatically collected and labeled training samples, is able to recognize crop and weeds with an accuracy of almost 94 percent. This result in acceptable weed hit rates and significant herbicide reductions. Spot-spraying on the weeds only becomes economically feasible.

Paper Details

Date Published: 11 November 1999
PDF: 11 pages
Proc. SPIE 3897, Advanced Photonic Sensors and Applications, (11 November 1999); doi: 10.1117/12.369309
Show Author Affiliations
Filip Feyaerts, Katholieke Univ. Leuven (Belgium)
P. Pollet, Katholieke Univ. Leuven (Belgium)
Luc J. Van Gool, Katholieke Univ. Leuven (Belgium)
Patrick Wambacq, Katholieke Univ. Leuven (Belgium)


Published in SPIE Proceedings Vol. 3897:
Advanced Photonic Sensors and Applications
Robert A. Lieberman; Anand Krishna Asundi; Hiroshi Asanuma, Editor(s)

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