Next Article in Journal
A 13-Bit, 12-ps Resolution Vernier Time-to-Digital Converter Based on Dual Delay-Rings for SPAD Image Sensor
Previous Article in Journal
A Grid-Based Framework for Collective Perception in Autonomous Vehicles
Article

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

1
Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA
2
Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
3
Division of Food Sciences, University of Missouri, Columbia, MO 65211, USA
4
Department of Food Sciences, The Pennsylvania State University, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 742; https://doi.org/10.3390/s21030742
Received: 11 November 2020 / Revised: 14 January 2021 / Accepted: 19 January 2021 / Published: 22 January 2021
(This article belongs to the Section Sensing and Imaging)
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples. View Full-Text
Keywords: plant disease; spectral statistics; machine learning; 2D-CNN; 3D-CNN; grapevine vein-clearing virus (GVCV) plant disease; spectral statistics; machine learning; 2D-CNN; 3D-CNN; grapevine vein-clearing virus (GVCV)
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, C.; Sagan, V.; Maimaitiyiming, M.; Maimaitijiang, M.; Bhadra, S.; Kwasniewski, M.T. Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors 2021, 21, 742. https://doi.org/10.3390/s21030742

AMA Style

Nguyen C, Sagan V, Maimaitiyiming M, Maimaitijiang M, Bhadra S, Kwasniewski MT. Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors. 2021; 21(3):742. https://doi.org/10.3390/s21030742

Chicago/Turabian Style

Nguyen, Canh, Vasit Sagan, Matthew Maimaitiyiming, Maitiniyazi Maimaitijiang, Sourav Bhadra, and Misha T. Kwasniewski. 2021. "Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning" Sensors 21, no. 3: 742. https://doi.org/10.3390/s21030742

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop