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NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

by Liu Zhang 1,2, Zhenhong Rao 3 and Haiyan Ji 1,2,*
1
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
2
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
3
College of Science, China Agricultural University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3147; https://doi.org/10.3390/s19143147
Received: 10 June 2019 / Revised: 14 July 2019 / Accepted: 15 July 2019 / Published: 17 July 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable. View Full-Text
Keywords: hyperspectral imaging; omethoate; wheat kernels; multivariate scatter correction; neighborhood component analysis; support vector machine; object-wise hyperspectral imaging; omethoate; wheat kernels; multivariate scatter correction; neighborhood component analysis; support vector machine; object-wise
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Zhang, L.; Rao, Z.; Ji, H. NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface. Sensors 2019, 19, 3147.

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