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Validation of an Operator-Assisted Module to Measure Weed and Crop Leaf Cover by Digital Image Analysis

Published online by Cambridge University Press:  12 June 2017

Mathieu Ngouajio ,
Claudel Lemieux ,
Jean-Jacques Fortier ,
Denis Careau  and
Gilles D. Leroux
Mathieu Ngouajio
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
Claudel Lemieux
Affiliation:
Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Sainte-Foy, QC, Canada G1V 2J3
Jean-Jacques Fortier
Affiliation:
Société de Mathématiques Appliquées Inc., 59 d'Auteuil Street, Quebec, QC, Canada G1R 4C2
Denis Careau
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
Gilles D. Leroux
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
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Abstract

The practical application of yield loss prediction models using relative leaf area of weeds is limited due to the lack of a quick and accurate method of leaf area estimation. Leaf cover (the vertical projection of plant canopy on the ground) can be used to approximate leaf area at early stages of plant development. An automated digital image analysis system for measuring leaf cover has been developed. The system has an operator-assisted module aimed at validating the automated functions. The objective of this research was to demonstrate the accuracy of the operator-assisted module under different weed–crop conditions. A laboratory experiment was conducted using simulated weed–crop populations. Two additional field experiments were conducted using corn in competition with: (1) common lambsquarters, barnyardgrass, or a mixture of both species, and (2) a natural weed community. In the laboratory experiment, a narrow linear relation was observed between leaf cover estimated with the operator-assisted module and leaf area measured with an optical area meter (r2 > 0.98). In field experiments, the regression between corn leaf cover estimated by the operator-assisted module and corn leaf area measured with the optical area meter was not as good (r2 < 0.55). The poor performance of the module was probably due to the overlapping and the architecture of corn leaves (especially unexpanded leaves). Nevertheless, the system showed high precision in estimating leaf area of both grassy weeds and broadleaf weeds (r2 > 0.89). Generally, the accuracy of the estimates decreased as the growth stage became more advanced. Apart from its initial purpose as a calibration tool for the automated system, the operator-assisted module can have several potential research applications. It can be used: (1) as an alternative to destructive leaf area measurement at early stages of plant development, (2) as a tool in the study of plant competitive ability, and (3) as an objective and quantitative support to visual observations.

Keywords

Common lambsquarters, Chenopodium album L. # CHEALbarnyardgrass, Echinochloa crus-galli (L.) Beauv. # ECHCGcorn, Zea mays L.Machine visionpixel classificationleaf arealeaf coverCHEALECHCG
Type
Research
Information
Weed Technology , Volume 12 , Issue 3 , September 1998 , pp. 446 - 453
Copyright
Copyright © 1998 by the Weed Science Society of America 

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