[en] Weeds compete with crop plants for sunlight, moisture and nutrients and can have a detrimental impact on crop yields and quality if uncontrolled. They are destroyed by chemical, non chemical and integrated methods. To perform a site-specific weeds destruction, combination of these techniques with ground-based machine vision technology has high potential. Several methods exist to differentiate weeds from soil, between the rows. The more complicated problem is encountered when weeds are mixed to crops within the rows. Algorithms based on colorimetric or shape features are widely dependant on the variability of weeds and crops and are difficult to transpose from one situation to another. Measurement of plant height is a promising method, since at low spatial scale, the growthing speed is more uniform for the plants than for the weeds. This growing speed is function of the height and of a characteristic time, such as the number of days after sowing. To implement this method, active stereoscopy combined to an accurate measurement of the soil microrelief is required.
Astrand B. & Baerveldt A.-J., 2002. An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robots, 13, 21-35.
Astrand B. & Baerveldt A.-J., 2005. A vision based rowfollowing system for agricultural field machinery. Mechatronics, 15, 251-269.
Berge T., Aastveit A. & Fykse H., 2008. Evaluation of an algorithm for automatic detection of boad-leaved weeds in spring cereals. Precis. Agric., 9, 391-405.
Bertrand M. & Doré T., 2008. Comment intégrer la maîtrise de la flore adventice dans le cadre général d'un système de production intégrée? Innovations Agron., 3, 1-13.
Blackmore S., Stout B., Wang M. & Runov B., 2005. Robotic agriculture - the future of agricultural mechanization? In: Proceedings of the 5th European Conference on Precision Agriculture, 9-12/06/2005, Uppsala, Sweden, 621-628.
Boller E.F. et al., 2004. Integrated production: principles and technical guidelines. IOPC wprs Bull., 27(2).
Brown R.B. & Noble S.D., 2005. Site-specific weed management: sensing requirements. What do we need to see? Weed Sci., 53(2), 252-258.
Burgos-Artizzu X., Ribeiro A., Tellaeche A. & Pajares G., 2010. Analysis of natural images processing for the extraction of agricultural elements. Image Vision Comput., 28, 138-149.
Burks T.F., Shearer S.A. & Payne F.A., 2000. Classification of weed species using color texture features and discriminant analysis. Trans. ASAE, 43(2), 441-448.
Burks T.F., Shearer S.A., Heath J.R. & Donohue K.D., 2005. Evaluation of neural-networks classifiers for weed species discrimination. Biosystems Eng., 91(3), 293-304.
Comaniciu D. & Meer P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5), 603-619.
Commission européenne, 2009. Directive 2009/128/CE du Parlement européen et du Conseil du 21 octobre 2009 instaurant un cadre d'activité communautaire pour parvenir à une utilisation des pesticides compatible avec le développement durable. J. Off. Union Eur., 24.11.2009, L309, 71-85.
Feyaerts F. & Van Gool L., 2001. Multi-spectral vision system for weed detection. Pattern Recognit. Lett., 22, 667-674.
Franz E., Gebhardt M.R. & Unklesbay K.B., 1991a. Shape description of completely visible and partially occluded leaves for identifying plants in digital images. Trans. ASAE, 34(2), 673-681.
Franz E., Gebhardt M.R. & Unklesbay K.B.,1991b. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. ASAE, 34(2), 682-687.
Gebhardt S. & Kühbauch W., 2007. A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precis. Agric., 8(1), 1-13.
Gee C., Bonvarlet L., Magnin-Robert J.B. & Guillemin J.P., 2004. Weed discrimination by reflectance measurements using neural networks. In: 12e Colloque international sur la biologie des mauvaises herbes, 31.08-02.09, Dijon, France. Paris: Association Française de Protection des Plantes (AFPP), 487-494.
Gerhards R. & Christensen S., 2003. Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res., 43, 385-392.
Hemming J. & Rath T., 2001. Computer-vision-based weed identification under field conditions using controlled lighting. J. Agric. Eng. Res., 78(3), 233-243.
Jones G., Gee C. & Truchetet F., 2009. Assessment of an inter-row weed infestation rate on simulated agronomic images. Comput. Electron. Agric., 67(1-2), 43-50.
Kurstjens D.A.G. & Kropff M.J., 2001. The impact of uprooting and soil-covering on the effectiveness of weed harrowing. Weed Res., 41, 211-228.
Lamb D.W. & Brown R.B., 2001. Remote-sensing and mapping of weeds in crops. J. Agric. Eng. Res., 78, 117-125.
Lee W.S., Slaughter D.C. & Giles D.K., 1999. Robotic weed control system for tomatoes. Precis. Agric., 1, 95-113.
Lee W.S. & Slaughter D.C., 2004. Recognition of partially occluded plant leaves using a modified watershed algorithm. Trans. ASAE, 47(4), 1269-1280.
Leemans V. & Destain M.-F., 2006. Line cluster detection using a variant of the Hough transform for culture row localisation. Image Vision Comput., 24, 541-550.
Marchant J.A. & Onyango C.M., 2003. Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soildiscrimination. Comput. Electron. Agric., 39, 3-22.
Meyer G.E., Hindman T.W. & Lakshmi K., 1998. Machine vision detection parameters for plant species identification. Bellingham, WA, USA: SPIE.
Nielsen M., Andersen H., Slaughter D. & Giles D., 2004. Detecting leaf features for automatic weed control using trinocular stereo vision. In: International Conference on Precision Agriculture 2004, Minneapolis, MN, USA.
Nieuwenhuizen A.T. et al., 2007. Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision. Precis. Agric., 8, 267-278.
Okamoto H., Murata T., Kataoka T. & Hata S., 2007. Plant classification for weeds detection using hyperspectral imaging with wavelet analysis. Weed Biol. Manage., 7(1), 31-37.
Onyango C.M. & Marchant J.A., 2003. Segmentation of row crop plants from weeds using colour and morphology. Comput. Electron. Agric., 39(3), 141-155.
Parish S., 1990. A review of non-chemical weed control techniques. Biol. Agric. Hortic., 7, 117-137.
Piron A. et al., 2008a. Selection of the most efficient wavelength bands for discriminating weeds from crop. Comput. Electron. Agric., 62, 141-148.
Piron A., Leemans V., Kleynen O. & Destain M.-F., 2008b. Determination of plant height for weed detection in stereoscopic images. In: Agricultural Engineering Conference 2008, Hersonissos, Greece.
Piron A., Leemans V., Lebeau F. & Destain M.-F., 2009. Improving in-row weed detection in multispectral stereoscopic images. Comput. Electron. Agric., 69, 73-79.
Piron A., van der Heijden F. & Destain M.-F., 2011. Weed detection in 3D images. Precis. Agric., 12(5), 607-622.
Shrestha D.S., Stewart B.L. & Birrell S.J., 2004. Video processing for early stage maize plant detection. Biosyst. Eng., 89(2), 119-129.
Slaughter D.C., Lanini W.T. & Giles D.K., 2004. Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans. ASAE, 47(6), 1907-1911.
Slaughter D.C., Giles D.K. & Downey D., 2008. Autonomous robotic weed control systems: a review. Comput. Electron. Agric., 61, 63-78.
Sogaard H.T., 2005. Weed classification by active shape models. Biosyst. Eng., 91(3), 271-281.
Steward B. & Tian L., 1999. Machine-vision weed density estimation for real-time, outdoor lighting conditions. Trans. ASAE, 42(6), 1897-1909.
Tellaeche A., Burgos-Artizzu X.P., Pajares G. & Ribeiro A., 2007. A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognit., 41(2), 521-530.
Tellaeche A., Pajares G., Burgos-Artizzu X.P. & Ribeiro A., 2010. A computer vision approach for weeds identification through Support Vector Machines. Appl. Soft Comput., doi:10.1016/j.asoc.2010.01.011.
Thill D.C., Lish J.M., Callihan R.H. & Bechinski E.J., 1991. Integrated weed management - a component of integrated pest management: a critical review. Weed Technol., 5, 648-656.
Thompson J.F., Stafford J.V. & Miller P.C.H., 1991. Potential for automatic weed detection and selective herbicide application. Crop Prot., 10, 254-259.
Thorp K.R. & Tian L.F., 2004. A review of remote sensing of weeds in agriculture. Precis. Agric., 5, 477-508.
Tian L. & Slaughter D.C., 1993. Computer vision identification of tomato seedlings in natural outdoor scenes. ASAE Paper, N°93-3608.
Tian L. & Slaughter D.C., 1998. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Comput. Electron. Agric., 21, 153-168.
Van Evert K.K. et al., 2006. A mobile field robot with visionbased detection of volounter potato plants in a corn crop. Weed Technol., 20(4), 853-861.
Vioix J.-B. et al., 2002. Spatial and spectral methods for weeds detection and localization. Eurasip J. Appl. Signal Process., 7, 679-685.
Vioix J.-B., Douzals J.-P. & Truchetet F., 2004. Development of a multispectral imagery device devoted to weed detection. J. Electron. Imaging, 13(3), 547-552.
Vrindts E., De Baerdemaeker J. & Ramon H., 2002. Weed detection using canopy reflection. Precis. Agric., 3(1), 63-80.
Woebbecke D.M., Meyer G.E., Von Bargen K. & Mortensen D.A., 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE, 38(1), 259-264.
Zheng L., Daming S. & Jingtao Z., 2010. Segmentation of green vegetation of crop canopy images on mean shift and Fisher linear discriminant. Pattern Recognit. Lett., 31(9), 920-925.
Zwiggelaar R., 1998. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot., 17(1), 189-206.