Spectroscopy and Spectral Analysis, Volume. 37, Issue 7, 2115(2017)
Detection of Fungal Disease on Tomato Leaves with Competitive Adaptive Reweighted Sampling and Correlation Analysis Methods
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 47 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 9159% in the trainingset and 9245% in thetesting set. It demonstrated that hyperspectral imaging technique can be used for detecton of grey mold disease on tomato leaves.
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WANG Hai-long, YANG Guo-guo, ZHANG Yu, BAO Yi-dan, HE Yong. Detection of Fungal Disease on Tomato Leaves with Competitive Adaptive Reweighted Sampling and Correlation Analysis Methods[J]. Spectroscopy and Spectral Analysis, 2017, 37(7): 2115
Received: Jul. 1, 2015
Accepted: --
Published Online: Aug. 30, 2017
The Author Email: Hai-long WANG (hl_wang@zju.edu.cn)