竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害

doi:10.3964/j.issn.1000-0593(2017)07-2115-05

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Abstract Detection of grey mold on tomato leaves using hyperspectral imaging technique based on competitive adaptive reweighted sampling (CARS) and correlation analysis werestudied in this paper. Hyperspectral images of eighty healthy and eighty infected tomato leaves were captured with hyperspectral imaging systemin the spectral region of 380～1 023 nm. Spectral reflectanceof region of interest (ROI) from corrected hyperspectral image was extracted with ENVI 4.7 software. The support vector machine (SVM) model was established based on full spectral wavelengths. It obtained a good result with the discriminated accuracy of 100% in both training and testing sets. Two novel wavelength selection methods named CARS and CA were carried out to select effective wavelengths, respectively. Five wavelengths (554, 694, 696, 738 and 880 nm) and four wavelengths (527, 555, 571 and 633 nm) were obtained. Then, CARS-SVM and CA-SVM models were established based on the new wavelengths. CARS-SVM modelobtained good results with the discriminated accuracy of 100% in both training and testing sets. CA-SVM modelalso performed well with the discriminated accuracy of 91.59% in the trainingset and 92.45% in thetesting set. It demonstrated that hyperspectral imaging technique can be used for detecton of grey mold disease on tomato leaves.

Key words：Hyperspectral imaging; Competitive adaptive reweighted sampling (CARS); Correlation analysis (CA); Support vector machines (SVM); Tomato; Grey mold

Received: 2015-07-01 Accepted: 2016-01-15

ZTFLH:

TP391

Corresponding Authors: BAO Yi-dan E-mail: ydbao@zju.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)07-2115-05 OR https://www.gpxygpfx.com/EN/Y2017/V37/I07/2115

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