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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #301587
Title: Determination of germination quality of cucumber (Cucumis sativus) seed by LED-induced hyperspectral reflectance imaging

Author
item CHANGYEUN, MO - National Academy Of Agricultural Science
item JONGGUK, LIM - National Academy Of Agricultural Science
item KANGJIN, LEE - National Academy Of Agricultural Science
item SUKWON, KANG - National Academy Of Agricultural Science
item Kim, Moon
item GIYOUNG, KIM - National Academy Of Agricultural Science
item CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Journal of Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2013
Publication Date: 11/1/2013
Citation: Changyeun, M., Jongguk, L., Kangjin, L., Sukwon, K., Kim, M.S., Giyoung, K., Cho, B. 2013. Determination of germination quality of cucumber (Cucumis sativus) seed by LED-induced hyperspectral reflectance imaging. Journal of Biosystems Engineering. 38(4):318-326.

Interpretive Summary: Nondestructive sensing technologies have been widely used to evaluate quality and safety attributes of various agricultural commodities. In this study, hyperspectral reflectance imaging using newly developed LED lights as the illumination source was investigated for assessing the viability of cucumber seeds. Results demonstrated that the reflectance imaging method can predict seed viability with 99.5% accuracy. The methods and results presented in this research will benefit produce growers, as well as agricultural engineers working to develop rapid means to assess produce seed quality.

Technical Abstract: Purpose: We developed a viability evaluation method for cucumber (Cucumis sativus) seed using hyperspectral reflectance imaging. Methods: Reflectance spectra of cucumber seeds in the 400 to 1000 nm range were collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) was developed to predict viable and non-viable seeds. Various ranges of spectra induced by four types of LEDs (Blue, Green, Red, and RGB) were investigated to develop the classification models. Results: PLS-DA models for spectra in the 600 to 700 nm range showed 98.5 % discrimination accuracy for both viable and non-viable seeds. Using images based on the PLS-DA model, the discrimination accuracy for viable and non-viable seeds was 100% and 99%, respectively Conclusions: Hyperspectral reflectance images made using LED light can be used to select high quality cucumber seeds.