19.6.3.7.1 Weed Detection

Chapter Contents (Back)
Weeds. Application, Weeds. Weeds in crops, close range analysis, not aerial photos.

Feyaerts, F., Van Gool, L.J.,
Multi-spectral vision system for weed detection,
PRL(22), No. 6-7, May 2001, pp. 667-674.
Elsevier DOI 0105
BibRef

Vioix, J.B.[Jean-Baptiste], Douzals, J.P.[Jean-Paul], Truchetet, F.[Frédéric], Assémat, L.[Louis], Guillemin, J.P.[Jean-Philippe],
Spatial and Spectral Methods for Weed Detection and Localization,
JASP(2002), No. 7, July 2002, pp. 679-685. 0208
BibRef

Foschi, P.G.[Patricia G.], Liu, H.[Huan],
Active learning for detecting a spectrally variable subject in color infrared imagery,
PRL(25), No. 13, 1 October 2004, pp. 1509-1517.
Elsevier DOI 0410
feature extraction, automatic classification, active learning, and experimental evaluation for water weed classification. BibRef

Watchareeruetai, U.[Ukrit], Takeuchi, Y.[Yoshinori], Matsumoto, T.[Tetsuya], Kudo, H.[Hiroaki], Ohnishi, N.[Noboru],
Computer vision based methods for detecting weeds in lawns,
MVA(17), No. 5, October 2006, pp. 287-296.
Springer DOI 0609
BibRef

Tellaeche, A.[Alberto], Burgos-Artizzu, X.P.[Xavier P.], Pajares, G.[Gonzalo], Ribeiro, A.[Angela],
A vision-based method for weeds identification through the Bayesian decision theory,
PR(41), No. 2, February 2008, pp. 521-530.
Elsevier DOI 0711
Bayesian estimation; Parzen's window; Decision making; Machine vision; Image segmentation; Weed identification; Precision agriculture BibRef

Somers, B., Delalieux, S., Verstraeten, W.W., Verbesselt, J., Lhermitte, S., Coppin, P.,
Magnitude- and Shape-Related Feature Integration in Hyperspectral Mixture Analysis to Monitor Weeds in Citrus Orchards,
GeoRS(47), No. 11, November 2009, pp. 3630-3642.
IEEE DOI 0911
BibRef

Burgos-Artizzu, X.P.[Xavier P.], Ribeiro, A.[Angela], Tellaeche, A.[Alberto], Pajares, G.[Gonzalo], Fernandez-Quintanilla, C.[Cesar],
Analysis of natural images processing for the extraction of agricultural elements,
IVC(28), No. 1, Januray 2010, pp. 138-149.
Elsevier DOI 1001
Computer vision; Precision agriculture; Weed detection; Parameter setting; Genetic algorithms BibRef

Hiremath, S.[Santosh], Tolpekin, V.A.[Valentyn A.], van der Heijden, G.[Gerie], Stein, A.[Alfred],
Segmentation of Rumex obtusifolius using Gaussian Markov random fields,
MVA(24), No. 4, May 2013, pp. 845-854.
Springer DOI 1304
Broad-leavd Dock, a weed. BibRef

Wong, W.K., Chekima, A.[Ali], Wee, C.C.[Choo Chee], Brendon, K.[Khoo], Marriappan, M.[Muralindran],
Modular-based classification system for weed classification using mixture of features,
IJCVR(3), No. 3, 2013, pp. 261-278.
DOI Link 1412
BibRef

Prema, P., Murugan, D.,
A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation,
ELCVIA(15), No. 1, 2016, pp. 27-59.
DOI Link 1608
BibRef

de Castro, A.I.[Ana I.], Torres-Sánchez, J.[Jorge], Peña, J.M.[Jose M.], Jiménez-Brenes, F.M.[Francisco M.], Csillik, O.[Ovidiu], López-Granados, F.[Francisca],
An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Sa, I.[Inkyu], Popovic, M.[Marija], Khanna, R.[Raghav], Chen, Z.[Zetao], Lottes, P.[Philipp], Liebisch, F.[Frank], Nieto, J.[Juan], Stachniss, C.[Cyrill], Walter, A.[Achim], Siegwart, R.[Roland],
WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef


Bah, M.D., Hafiane, A., Canals, R.,
Weeds detection in UAV imagery using SLIC and the hough transform,
IPTA17(1-6)
IEEE DOI 1804
Hough transforms, agriculture, agrochemicals, autonomous aerial vehicles, crops, geophysical image processing, precision agriculture BibRef

Kounalakis, T.[Tsampikos], Triantafyllidis, G.A.[Georgios A.], Nalpantidis, L.[Lazaros],
Vision System for Robotized Weed Recognition in Crops and Grasslands,
CVS17(485-498).
Springer DOI 1711
BibRef

Haug, S.[Sebastian], Ostermann, J.[Jörn],
A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks,
PlantType14(105-116).
Springer DOI 1504
BibRef

Haug, S.[Sebastian], Michaels, A.[Andreas], Biber, P.[Peter], Ostermann, J.[Jorn],
Plant classification system for crop /weed discrimination without segmentation,
WACV14(1142-1149)
IEEE DOI 1406
Accuracy BibRef

Shi, C.J.[Chang-Jiang], Ji, G.R.[Guang-Rong],
Recognition Method of Weed Seeds Based on Computer Vision,
CISP09(1-4).
IEEE DOI 0910
BibRef

Chapron, M., Boissard, P., Assemat, L.,
A Multiresolution Based Method for Recognizing Weeds in Corn Fields,
ICPR00(Vol II: 303-306).
IEEE DOI 0009
BibRef

Sánchez, A.J., Marchant, J.A.,
Fusing 3D Information for Crop/weeds Classification,
ICPR00(Vol IV: 295-298).
IEEE DOI 0009
Close range images. BibRef

Chapron, M., Martin-Chefson, L., Assemat, L., Boissard, P.,
A Multiresolution Weed Recognition Method based on Multispectral Image Processing,
SCIA99(Image Analysis). BibRef 9900

Chapron, M., Khalfi, K., Boissard, P., and Assemat, L.,
Weed Recognition by Color Image Processing,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Pollen Detection, Analysis .


Last update:Nov 12, 2018 at 11:26:54