19.6.3.7.3 Weed Detection, Close Range

Chapter Contents (Back)
Weeds. Application, Weeds. Weeds in crops, close range analysis, not aerial photos. Aerial analysis for weeds:
See also Invasive Plants, Weeds, Exotic Plants.

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

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

Madsen, S.L.[Simon Leminen], Mathiassen, S.K.[Solvejg Kopp], Dyrmann, M.[Mads], Laursen, M.S.[Morten Stigaard], Paz, L.C.[Laura-Carlota], Jørgensen, R.N.[Rasmus Nyholm],
Open Plant Phenotype Database of Common Weeds in Denmark,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef


Galloway, A., Taylor, G.W., Ramsay, A., Moussa, M.,
The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment,
CRV17(361-366)
IEEE DOI 1804
aquaculture, computer vision, feedforward neural nets, image annotation, image segmentation, Ciona17 dataset, semantic segmentation 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:May 10, 2021 at 18:51:10