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浙江大学学报（农业与生命科学版）
  2012, Vol. 38 Issue (3): 311-317    DOI: 10.3785/j.issn.1008-9209.2012.03.012
Agricultural sciences     
Early detection of gray mold on eggplant leaves using hyperspectral imaging technique.
FENG Lei1，ZHANG Derong2， CHEN Shuangshuang1，FENG Bin3, XIE Chuanqi1，CHEN Youyuan4，HE Yong1
1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; 2.Ningbo Institute of Technology, Zhejiang University, Ningbo， Zhejiang 315000, China; 3.National Agriculture Exhibition Center, Beijing 100026, China; 4. Institute of Biotechnology, Zhejiang University, Hangzhou 310058, China
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Abstract  Early detection of gray mold on eggplant leaves using hyperspectral imaging technique was proposed. Hyperspectral images of 120 eggplant samples were captured by hyperspectral imaging system, and the spectral region was from 380 to 1031 nm. The pictures on three feature wavelengths were selected by principal component analysis (PCA), which was a good method to reduce the dimension of hyperspectral data. Eight feature variables were extracted by texture analysis based on gray level cooccurrence matrix (GLCM) after choosing the region of interest (ROI) of 200 × 150, which were mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, correlation respectively, thus 24 feature variables in total for three feature images. Successive projections algorithm (SPA) was executed on 24 feature variables, 13 feature variables in which were extracted as the input of the least square support vector machines (LSSVM) model, and the accurate rate of the model was 97.5%. It is showed that it is feasible for early detection of gray mold on eggplant leaves by hyperspectral imaging technique.

Published: 24 May 2012
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Articles by authors
FENG Lei
ZHANG Derong
CHEN Shuangshuang
FENG Bin
XIE Chuanqi
CHEN Youyuan
HE Yong
Cite this article:

FENG Lei，ZHANG Derong， CHEN Shuangshuang，FENG Bin, XIE Chuanqi，CHEN Youyuan，HE Yong. Early detection of gray mold on eggplant leaves using hyperspectral imaging technique.. , 2012, 38(3): 311-317.

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http://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2012.03.012     OR     http://www.zjujournals.com/agr/Y2012/V38/I3/311


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