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See detailWeed detection in 3D images
Piron, Alexis ULg; Van Der Heijden, F.; Destain, Marie-France ULg

in Precision Agriculture (2011), 12(5), 607-622

Machine vision has been successfully used for mechanical destruction of weeds between rows of crops. Knowledge of the position of the rows where crops should be growing and the assumption that plants ... [more ▼]

Machine vision has been successfully used for mechanical destruction of weeds between rows of crops. Knowledge of the position of the rows where crops should be growing and the assumption that plants growing outside such positions are weeds may be used in such systems. However for many horticultural crops, the automatic removal of weeds from inside a row or bands of crops in which the weeds are mixed with plants in a random manner is not solved. The aim of this study was to verify that plant height is a discriminating parameter between crop and weed at early growth stages, as weeds and crops grow at different speeds. Plant height was determined by using an active stereoscopy technique, based on a time multiplexing coded structured light developed to take into account the specificities of the small scale scene, namely occlusion and thin objects, internal reflections and high dynamic range. The study was conducted on two carrot varieties sown at commercial density. Different weed species were present at the time of data acquisition. To accurately represent plant height taking into account the ground irregularities, a new parameter called ‘corrected plant height’ was computed. This parameter was the distance between plant pixels and the actual ground level under them obtained by fitting a surface and seen from a reconstructed point of view corresponding to a camera’s optical axis perpendicular to the ridge plane. The overall classification accuracy without correction was 66% whereas it reached 83% by using the corrected plant height. [less ▲]

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See detailPlant leaf roughness analysis by texture classification with generalized Fourier descriptors in a dimensionality reduction context
Journaux, L.; Simon, J.-C.; Destain, Marie-France ULg et al

in Precision Agriculture (2011), 12(3), 345-360

In the context of plant leaf roughness analysis for precision spraying, this study explores the capability and the performance of some combinations of pattern recognition and computer vision techniques to ... [more ▼]

In the context of plant leaf roughness analysis for precision spraying, this study explores the capability and the performance of some combinations of pattern recognition and computer vision techniques to extract the roughness feature. The techniques merge feature extraction, linear and nonlinear dimensionality reduction techniques, and several kinds of methods of classification. The performance of the methods is evaluated and compared in terms of the error of classification. The results for the characterization of leaf roughness by generalized Fourier descriptors for feature extraction, kernel-based methods such as support vector machines for classification and kernel discriminant analysis for dimensionality reduction were encouraging. These results pave the way to a better understanding of the adhesion mechanisms of droplets on leaves that will help to reduce and improve the application of phytosanitary products and lead to possible modifications of sprayer configurations. [less ▲]

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See detailWeed detection and/or destruction
Piron, Alexis ULg; Destain, Marie-France ULg

Patent (2009)

A method of determining the position of weeds growing in soil amongst crops comprising the steps of:- Using a camera to acquiring a stereoscopic image of plants growing on soil; - Segmentation plant ... [more ▼]

A method of determining the position of weeds growing in soil amongst crops comprising the steps of:- Using a camera to acquiring a stereoscopic image of plants growing on soil; - Segmentation plant pixels and soil pixels; - Creating a modelised height profile of the soil at positions at which soil pixels have not been captured by the stereoscopic image based on information derived from adjacent soil pixels; - Determining a corrected plant height at plant pixels representing the distance between the plant pixel and the modelised soil underneath; - Differentiating between weeds and crops by ciomparing the corrected plant height with an expected crop height. Once the position of a weed has been determined, it may be destroyed, for example by heat applied to the identified position or by a robotic arm. [less ▲]

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See detailDetecting weeds by artificial vision in carrots: towards optimization of herbicide use
Piron, Alexis ULg; Destain, Marie-France ULg

Conference (2009, April 03)

A method of determining the position of weeds growing in soil amongst horticultural crops is described.

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See detailImproving in-row weed detection in multispectral stereoscopic images
Piron, Alexis ULg; Leemans, Vincent ULg; Lebeau, Frédéric ULg et al

in Computers & Electronics in Agriculture (2009), 69

Previous research has shown that plant height and spectral reflectance are relevant features to classify crop and weeds in organic carrots: classification based on height gave a classification accuracy ... [more ▼]

Previous research has shown that plant height and spectral reflectance are relevant features to classify crop and weeds in organic carrots: classification based on height gave a classification accuracy (CA) of up to 83% while classification based on a combination of three multispectral bands gave a CA of 72%. The first goal of this study was to examine the simultaneous use of both height and multispectral parameters. It was found that classification rate was only slightly improved when using a feature set comprising both height and multispectral data (2%). The second goal of this study was to improve the detection method based on plant height by setting an automatic threshold between crop and weeds heights, in their early growth stage. This threshold was based on crop row determination and peak detection in plant height probability density function, corresponding to the homogeneous crop population. Using this method, the CA was 82% while the CA obtained with optimal plant height limits is only slightly higher at 86%. [less ▲]

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See detailCoded structural light imaging system for weed detection in outdoor conditions
Destain, Marie-France ULg; Piron, Alexis ULg

Conference (2008, October)

A method of determining the position of weeds growing in soil amongst crops is described. Is uses a camera to acquire stereoscopic images of plants.

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See detailTexture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: an Overview Exploration
Journaux, Ludovic; Destain, Marie-France ULg; Miteran, Joel et al

in Lecture Notes in Computer Science (2008), 5064

In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and ... [more ▼]

In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database. © 2008 Springer-Verlag Berlin Heidelberg. [less ▲]

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See detailSelection of the most efficient wavelength bands for discriminating weeds from crop
Piron, Alexis ULg; Leemans, Vincent ULg; Kleynen, O. et al

in Computers & Electronics in Agriculture (2008), 62(2), 141-148

The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral ... [more ▼]

The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral device consisting of a black and white camera coupled with a rotating wheel holding 22 interference filters in the VIS-NIR domain. Measurements were performed over a period of 19 days, starting 1 week after crop emergence (early weeding can increase yields) and seven different weeds species were considered. The selection of the best filter combination was based on a quadratic discriminant analysis. The best combination of filters included three interference filters, respectively centred on 450, 550 and 700 nm. With this combination, the overall classification accuracy (CA) was 72%. When using only two filters, a slight degradation of the CA was noticed. When the classification results were reported on field images, a systematic misclassification of carrot cotyledons appears. Better results were obtained with a more advanced growth stage. (c) 2007 Elsevier B.V. All rights reserved. [less ▲]

Detailed reference viewed: 79 (23 ULg)
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See detailDetermination of plant height for weed detection in stereoscopic images
Piron, Alexis ULg; Leemans, Vincent ULg; Kleynen, Olivier et al

in AGENG 2008 Conference - Agricultural & Biosystems Engineering for a Sustainable World (2008)

The aim of this study was twofold. The first goal was to acquire high accuracy stereoscopic images of small-scale field scenes, the second to examine the potential of plant height as a discriminant factor ... [more ▼]

The aim of this study was twofold. The first goal was to acquire high accuracy stereoscopic images of small-scale field scenes, the second to examine the potential of plant height as a discriminant factor between crop and weed, within carrot rows. Emphasis was put on how to determine actual plant height taking into account the variable distance from camera to ground and ground irregularities for in-field measurements. Multispectral stereoscopic images were taken over a period of 19 days starting one week after crop emergence and seven weed species were considered. Images were acquired with a mobile vision system consisting in a filter wheel based multispectral camera and a video projector. The stereoscopy technique used belonged to the coded structured light family. The stereoscopic acquisition method yielded good results despite the numerous stereoscopic difficulties exhibited by the scenes. A plant height parameter as opposed to distance from camera to plant pixels gave better results for classification (classification accuracy of up [less ▲]

Detailed reference viewed: 69 (26 ULg)