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Sensors 2018, 18(7), 2077;

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
College of Agriculture & Biotechnology, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
Zhejiang Provincial Key Laboratory of Horticultural Plant Integrative Biology, Hangzhou 310058, China
The State Agriculture Ministry Laboratory of Horticultural Plant Growth, Development and Quality Improvement, Hangzhou 310058, China
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
Key Laboratory of Transplanting Equipment and Technology of Zhejiang Province, Hangzhou, 310018, China
South Taihu Agricultural Technology Extension Center in Huzhou, Zhejiang University, Huzhou 313000, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 9 May 2018 / Revised: 16 June 2018 / Accepted: 26 June 2018 / Published: 28 June 2018
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [2123 KB, uploaded 28 June 2018]   |  


Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400–1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400–1000 nm (visible range) and 900–1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way. View Full-Text
Keywords: hyperspectral imaging; mulberry leaves; Diaphania pyloalis; larvae; damage hyperspectral imaging; mulberry leaves; Diaphania pyloalis; larvae; damage

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Huang, L.; Yang, L.; Meng, L.; Wang, J.; Li, S.; Fu, X.; Du, X.; Wu, D. Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves. Sensors 2018, 18, 2077.

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