Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Abstract

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

“…PCA has been used as a well-established image compression method [41] preserving the image's total variance in the process of transformation and minimization of mean square approximate error [29,42]. It uses the second order statistics and a covariance matrix by projecting data into an orthogonal space to measure the highest eigenvalues which correspond to the maximum variance of the uncorrelated linearly transformed data [19].…”

Remote sensing bio-control damage on aquatic invasive alien plant species

“…However, the performance of the SVM algorithm is sensitive to the choice of kernel function and the setting of its associated parameters (Song et al, 2012). A study conducted by Liu et al (2010a) discriminated rice panicles of different health conditions by applying a PCA and SVM classification on hyperspectral reflectance data. Results showed that the overall accuracies of the SVM classifications with principle components derived from the raw, first, and second order reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the Kappa coefficients were 94.81, 98.71, and 94.82, respectively.…”

Plant Health Detection and Monitoring

“…Liu et al (2010a) applied neural networks and PCA to detect fungal disease of rice panicles in hyperspectral images in 350-2,500 nm acquired in a laboratory, and reported that four different infection levels were classified with 86-100 % accuracies. Liu et al (2010b) adopted principal component analysis (PCA) and support vector machine (SVM) to classify healthy and infected rice panicles by rice false smut (U. virens), whose hyperspectral images were acquired in the visible and NIR ranges in a laboratory, and reported over 96 % accuracies using the original spectral data, first derivatives, and second derivatives. For potato disease detection, Ray et al (2011) investigated detection of potato late blight disease using a hand-held spectroradiometer in 325-1,075 nm, utilized stepwise discriminant analysis and different VIs (NDVI, simple ratio (SR), soil adjusted vegetation index (SAVI), and red edge).…”