基于高光谱的番茄叶片斑潜蝇虫害检测

doi:10.3964/j.issn.1000-0593(2018)01-0253-05

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Abstract Tomato yield and farmers’ economic benefits will decrease when insect pest occurs in the growth of tomato plants. This study used hyperspectral technology combined with chemometrics methods to realize fast identification of tomato leaf LiriomyzaSativae Blanchard pest. A simple hyperspectral imaging system was developed, including a light source unit, and hyperspectral image acquisition unit and a data processing unit, and hyperspectral images of tomato leaves were collected through this system. Hyperspectral images were calibrated and spectral information was extracted from each image. Spectral angle mapping (SAM) analysis method and spectrum red edge parameters discriminant analysis (DA) method were adopted to identify tomato leaf Liriomyza Sativae Blanchard pest respectively. In the SAM analysis, normalization algorithm was utilized to preprocess hyperspectral data so as to eliminate redundant information in hyperspectral data and increase the differences between samples. Discriminant effects of tomato leaf pest were compared when different reflective spectrums of tomato leaf samples were used as test spectrums. It was found that when regarding the average reflectance spectrum of 100 tomato leaves infected by Liriomyzasativae Blanchard pest as the test spectrum, the overall recognition accuracy was higher, reaching to 96.5%. In spectrum red edge parameters discriminant analysis, 6 kinds of red edge information that red edge position, red edge amplitude, minimum amplitude, red edge area, location of minimum chlorophyll absorption, and the ratio of red edge amplitude to minimum amplitude were extracted from tomato leaves’ spectral data. Discriminant analysis method was used to develop discriminant model of tomato leaf LiriomyzaSativae Blanchard pest, discriminant effects of distance discriminant analysis, Fisher discriminant analysis, and Bayes discriminant analysis were compared. Comparison results indicated that Fisher discriminant analysis generated the best discriminant effect. The discriminant accuracy was 96.0% for validation set, while distance discriminant analysis produced the worst discriminant effect, with 88.0% discriminant accuracy. Research results showed that using hyperspectral technology to identify Liriomyza sativae Blanchard pest was feasible.

Key words：Hyperspectral technology; Tomato; Pest; SAM; Red edge parameter; DA

Received: 2017-02-10 Accepted: 2017-06-28

ZTFLH:

O657.3

Corresponding Authors: WANG Xiu E-mail: wangx@nercita.org.cn

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)01-0253-05 OR http://www.gpxygpfx.com/EN/Y2018/V38/I01/253