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Using hyperspectral imaging technology to identify diseased tomato leaves

文献类型： 外文期刊

作者： Li, Cuiling 1 ; Wang, Xiu 1 ; Zhao, Xueguan 1 ; Meng, Zhijun 1 ; Zou, Wei 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Tomato leaf;disease;hyperspectral imaging technology;spectrum angle matching;red edge parameter

期刊名称：INFRARED, MILLIMETER-WAVE, AND TERAHERTZ TECHNOLOGIES IV

ISSN： 0277-786X

年卷期： 2016 年 10030 卷

页码：

收录情况： SCI

摘要： In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

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[1]Tomato Leaf Liriomyza Sativae Blanchard Pest Detection Based on Hyperspectral Technology. Li Cui-ling,Jiang Kai,Ma Wei,Wang Xiu,Meng Zhi-jun,Zhao Xue-guan,Song Jian,Li Cui-ling,Jiang Kai,Ma Wei,Wang Xiu,Meng Zhi-jun,Zhao Xue-guan,Song Jian. 2018

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