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Weed Leaf Recognition in Complex Natural Scenes by Model-Guided Edge Pairing

Benoit de Mezzo 1 Gilles Rabatel 1 Christophe Fiorio 2
2 ARITH - Arithmétique informatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : New weeding strategies for pesticide reduction rely on the spatial distribution and characterisation of weed populations. For this purpose, weed identification can be done by machine vision applied in the field. Due to the scene complexity, a priori knowledge on the searched shape is valuable to enhance the image segmentation process. We propose here an approach based on a primary analysis of object boundary pieces in the image. This analysis relies on shape modelling, and leads to the generation of hypotheses about actual leaves in the scene. First results are presented, and further developments are proposed.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00191956
Contributor : Christine Carvalho de Matos <>
Submitted on : Monday, November 26, 2007 - 11:43:50 AM
Last modification on : Wednesday, June 3, 2020 - 3:47:53 AM
Document(s) archivé(s) le : Friday, November 25, 2016 - 5:46:11 PM

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  • HAL Id : lirmm-00191956, version 1

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Benoit de Mezzo, Gilles Rabatel, Christophe Fiorio. Weed Leaf Recognition in Complex Natural Scenes by Model-Guided Edge Pairing. ECPA'03: 4th European Conference on Precision Agriculture, 2003, Berlin, Germany. pp.141-147. ⟨lirmm-00191956⟩

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