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Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control

Published online by Cambridge University Press:  20 January 2017

David C. Slaughter,
D. Ken Giles,
Steven A. Fennimore and
Richard F. Smith
David C. Slaughter*
Affiliation:
Biological and Agricultural Engineering Department, University of California–Davis, Davis, CA 95616
D. Ken Giles
Affiliation:
Biological and Agricultural Engineering Department, University of California–Davis, Davis, CA 95616
Steven A. Fennimore
Affiliation:
Department of Plant Sciences, University of California–Davis, Salinas, CA 93905
Richard F. Smith
Affiliation:
University of California Cooperative Extension, Salinas, CA 93901
*
Corresponding author's E-mail: dcslaughter@ucdavis.edu
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Abstract

Multispectral images of leaf reflectance in the visible and near infrared region from 384 to 810 nm were used to establish the feasibility of developing a site-specific classifier to distinguish lettuce plants from weeds in California direct-seeded lettuce fields. An average crop vs. weed classification accuracy of 90.3% was obtained in a study of over 7,000 individual spectra representing 150 plants. The classifier utilized reflectance values from a small spatial area (3 mm diameter) of the leaf in order to allow the method to be robust to occlusion and to eliminate the need to identify leaf boundaries for shape-based machine vision recognition. Reflectance spectra were collected in the field using equipment suitable for real-time operation as a weed sensor in an autonomous system for automated weed control.

Keywords

Lettuce, Lactuca sativa L. ‘Capitata’ and ‘Crispa’Machine visionmultispectral imagesreflectanceroboticsweed detectionweed recognition

Information

Type
Symposium
Information
Weed Technology , Volume 22 , Issue 2 , June 2008, pp. 378 - 384
Copyright
Copyright © Weed Science Society of America 

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