title: [Different wavelengths selection methods for identification of early blight on tomato leaves by using hyperspectral imaging technique].
abstract: Identification of early blight on tomato leaves by using hyperspectral imaging technique based on different effective wavelengths selection methods (successive projections algorithm, SPA; x-loading weights, x-LW; gram-schmidt orthogonaliza-tion, GSO) was studied in the present paper. Hyperspectral images of seventy healthy and seventy infected tomato leaves were obtained by hyperspectral imaging system across the wavelength range of 380-1023 nm. Reflectance of all pixels in region of interest (ROI) was extracted by ENVI 4. 7 software. Least squares-support vector machine (LS-SVM) model was established based on the full spectral wavelengths. It obtained an excellent result with the highest identification accuracy (100%) in both calibration and prediction sets. Then, EW-LS-SVM and EW-LDA models were established based on the selected wavelengths suggested by SPA, x-LW and GSO, respectively. The results showed that all of the EW-LS-SVM and EW-LDA models performed well with the identification accuracy of 100% in EW-LS-SVM model and 100%, 100% and 97. 83% in EW-LDA model, respectively. Moreover, the number of input wavelengths of SPA-LS-SVM, x-LW-LS-SVM and GSO-LS-SVM models were four (492, 550, 633 and 680 nm), three (631, 719 and 747 nm) and two (533 and 657 nm), respectively. Fewer input variables were beneficial for the development of identification instrument. It demonstrated that it is feasible to identify early blight on tomato leaves by using hyperspectral imaging, and SPA, x-LW and GSO were effective wavelengths selection methods.
authors.forename: Shu-Xi
authors.initials: SX
authors.lastname: Cheng
authors.forename: Chuan-Qi
authors.initials: CQ
authors.lastname: Xie
authors.forename: Qiao-Nan
authors.initials: QN
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: He
authors.forename: Yong-Ni
authors.initials: YN
authors.lastname: Shao
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D018551:Solanum lycopersicum / Q000382:microbiology*; D008962:Models, Theoretical; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology*; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine