Weed detection is a key problem of spot spraying that could reduce the herbicide usage. Spectral information of plants is very useful to detect weeds in real-time for the fast response time. However, the cost of an imaging spectrograph-based weed detection system is too high. Therefore, the main objective of this study was to explore a method to classify crop and weed plants using the spectral information in the visible light captured by a CCD camera. One approach to weed classification was to directly use of G and R component of RGB color space. Another was to utilize the spectral information among the green band that hue was regarded as wavelength, and saturation was represented as reflectance. The result of statistic analysis showed that both of them using the G-R and H-S optimized segmentation line of crop and weeds could be used to detect weed (lixweed tansymnustard) from wheat fields. Moreover, the method of using the H-S optimized model could avoid the affect of lighting.
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Borregaard T., H. Nielsen, L. Norgaard, et al., Crop-weed discrimination by line imaging spectroscopy, Journal of Agricultural Engineering Research, 2000, 75:389-400
Brown R. B., J.-P.G.A. Steckler, Prescription maps for spatially variable herbicide application in no-till corn, Transactions of the ASAE, 1995, Vol. 38, No. 6 :1659-1666
Burks T.F., S.A. Shearer, F.A. Payne, Classification of weed species using color texture features and discriminant analysis, Transactions of ASAE, 2000, Vol. 43, No. 2:441-448
Feyaerts F., L. van Gool, Multi-spectral vision system for weed detection, Pattern Recognition Letters, 2001, 22:667-674
Lee W.S., D.C. Slaughter, D.K. Giles, Robotic Weed control system for tomatoes, Precision Agriculture, 1999, 1:95-113
Perez A.J., F. Lopez, J.V. Benlloch, et al., Color and shape analysis techniques for weed detection in cereal fields, Computers and electronics in agriculture, 2000, 25:197-212
Robert J. Baron, Trever G. Crowe, Thomas M. Wolf, Dual camera measurement of crop canopy using reflectance, AIC 2002 Meeting, CSAE/SCGR Program Saskatoon, July 14-17, 2002
Stafford J.V., J.M. Le Bars, B. Ambler, A hand-held data logger with integral GPS for producing weed maps by field walking, Computers and Electronics in Agriculture, 1996, 14:235-247
Tang L., L.F. Tian, B.L. Steward, et al., Texture-based weed classification using Gabor wavelets and neural network for real-time selective herbicide applications, ASAE, 1999, Paper No. 993036
Thompson J.F., J.V. Stafford, P.C.H. Miller, Potential for Automatic Weed Detection and Selective Herbicide Application, Crop Protection, 1991, 10:254-259
Tian L., Development of a sensor-based precision herbicide application system. Computers and electronics in agriculture, 2002, 36:133-149
Vrindts E., J. De Baerdemaeker, Using spectral information for weed detection in field circumstances, Presented at AgEng 2000 Warwick, 2000, EurAgEng Paper No. 00-PA-010
Vrindts Els, Automatic recognition of weeds with optical techniques as basis for site-specific spraying, [Dissertations DE Agricultura]. Katholieke Universititeit Leuven, 2000
Yao H., L.F. Tian, L. Tang, et al., Smart sprayer performance simulation. ASAE, 1999, Paper No. 99-1103
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Mao, W., Hu, X., Zhang, X. (2008). Weed Detection Based on the Optimized Segmentation Line of Crop and Weed. In: Li, D. (eds) Computer And Computing Technologies In Agriculture, Volume II. CCTA 2007. The International Federation for Information Processing, vol 259. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77253-0_27
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DOI: https://doi.org/10.1007/978-0-387-77253-0_27
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