SVM-based spectral recognition of corn and weeds at seedling stage in fields

• Published in: Guang pu xue yu guang pu fen xi Vol. 29; no. 7; p. 1906
• Main Authors: Deng, Wei, Zhang, Lu-Da, He, Xiong-Kui, Mueller, J, Zeng, Ai-Jun, Song, Jian-Li, Liu, Ya-Jia, Zhou, Ji-Zhong, Chen, Ji, Wang, Xu
• Format: Journal Article
• Language: Chinese
• Published: China 01.07.2009
• ISSN: 1000-0593
• DOI: 10.3964/j.issn.1000-0593(2009)07-1906-05

A handheld FieldSpec 3 Spectroradiometer manufactured by ASD Incorporated Company in USA was used to measure the spectroscopic data of canopies of seedling corns, Dchinochloa crasgalli, and Echinochloa crusgalli weeds within the 350-2 500 nm wavelength range in the field. Each canopy was measured five times continuously. The five original spectroscopic data were averaged over the whole wavelength range in order to eliminate random noise. Then the averaged original data were converted into reflectance data, and the unsmooth parts of reflectance spectral curves with large noise were removed. The effective wavelength range for spectral data process was selected as 350-1 300 and 1 400-1 800 nm. Support vector machine (SVM) was chosen as a method of pattern recognition in this paper. SVM has the advantages of solving the problem of small sample size, being able to reach a global optimization, minimization of structure risk, and having higher generalization capability. Two classes of classifier SVM models were buil