Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Abstract

Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging

“…Second derivative (2‐Der) is a commonly used spectral pre‐processing method with the advantages of overlapping peak removal, baseline correction, and the ability to increase the resolution of overlapping peaks . It can enhance signals related to chemical composition.…”

“…Second derivative (2-Der) is a commonly used spectral pre-processing method with the advantages of overlapping peak removal, baseline correction, and the ability to increase the resolution of overlapping peaks. 28,29 It can enhance signals related to chemical composition. Spectral peaks with large absolute values can be selected as OWs, and used to predict the quality parameters of the samples.…”

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

“…A variety of kernel functions exist, namely, linear kernel function, radial basis function (RBF), polynomial kernel function, and polynomial function. In the present study, SVM was built using RBF because previous researches confirm that RBF is a more compatible supported kernel function . The penalty coefficient ( c ) of the SVM model and the kernel width ( g ) of the kernel function must be determined.…”

“…In the present study, SVM was built using RBF because previous researches confirm that RBF is a more compatible supported kernel function. [26][27][28] The penalty coefficient (c) of the SVM model and the kernel width (g) of the kernel function must be determined. The optimal combination of (c, g) was determined by a grid-search procedure and the ranges of c and g were both 2 −10 to 2 10 .…”

Nondestructive detection of apple crispness via optical fiber spectroscopy based on effective wavelengths

“…In recent years, spectroscopy has been proposed as a nondestructive crispness detection technique. Some studies have explored spectroscopy as a method for the nondestructive analysis of other crops and fruits, research which has included apples (Dong & Guo, ; Kong, Zhang, Huang, Liu, & He, ; Li et al, ; Peng et al, ; Sun, Künnemeyer, Mcglone, & Rowe, ). Spectroscopy techniques were also used in other fields such as oil and soil detection (Feng et al, ; He et al, ; Nie, Dong, He, & Xiao, ).…”