Journal article
Visible-near infrared spectroradiometry-based detection of grapevine leafroll-associated virus 3 in a red-fruited wine grape cultivar
Computers and electronics in agriculture, Vol.162, pp.165-173
07/2019
Handle:
https://hdl.handle.net/2376/116608
Abstract
•Leaf level VIS–NIR sensing was assessed for grapevine GLRaV–3 detection.•Salient feature wavelengths were 1001, 1027 and 1052 nm.•Features were robust to detect GLRaV–3 symptoms at asymptomatic stage.•QDA performed better than Naïve Bayes in classifying infected samples.
Grapevine leafroll disease (GLD) is one of the major threats to wine grapes (Vitis vinifera) causing substantial economic losses to the growers. This study was undertaken to evaluate the applicability of visible and near infrared (VIS-NIR) spectroradiometery as a rapid, robust and non–destructive optical sensing method for the detection of Grapevine leafroll-associated virus 3 (GLRaV-3) at different phenological stages in a red-berried wine grape cultivar. Using VIS-NIR spectroradiometer, data was collected from the healthy and GLRaV-3-infected leaf samples from cv. Cabernet Sauvignon for two seasons at specific intervals during asymptomatic and symptomatic stages of the disease. Fiber optic leaf clip was used to collect spectral responses from grapevine leaves under field conditions. Salient feature extraction using stepwise multilinear regression and partial least square regression methods showed significant differences between healthy and virus–infected leaves in the visible (351, 377, 501, 526, 626 and 676 nm) and near infrared (701, 726, 826, 901, 951, 976, 1001, 1027, 1052 and 1101 nm) regions. Spectral wavelengths from near infrared region (1001, 1027 and 1052 nm) were validated at different phenological stages spanning both asymptomatic and symptomatic stages of the disease. Selected spectral wavelengths demonstrated robustness in virus detection with overall classification accuracies in the range of 75–99% using quadratic discriminant analysis (QDA) classifier. QDA based classification accuracies for healthy, infected and overall classes were significantly higher compared to Naïve Bayes classifier. The accuracy for virus detection during asymptomatic stages was not significantly different from the symptomatic phase, indicating reliability of the selected features for early detection of GLRaV–3–infected grapevines.
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Details
- Title
- Visible-near infrared spectroradiometry-based detection of grapevine leafroll-associated virus 3 in a red-fruited wine grape cultivar
- Creators
- Rajeev Sinha - Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems (CPAAS), Irrigated Agriculture Research and Extension Center (IAREC), Washington State University, 24106 N. Bunn Rd, Prosser, WA 99350, USALav R Khot - Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems (CPAAS), Irrigated Agriculture Research and Extension Center (IAREC), Washington State University, 24106 N. Bunn Rd, Prosser, WA 99350, USAAnura P Rathnayake - Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems (CPAAS), Irrigated Agriculture Research and Extension Center (IAREC), Washington State University, 24106 N. Bunn Rd, Prosser, WA 99350, USAZongmei Gao - Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems (CPAAS), Irrigated Agriculture Research and Extension Center (IAREC), Washington State University, 24106 N. Bunn Rd, Prosser, WA 99350, USARayapati A Naidu - Department of Plant Pathology, IAREC, Washington State University, 24106 N. Bunn Rd, Prosser, WA 99350, USA
- Publication Details
- Computers and electronics in agriculture, Vol.162, pp.165-173
- Academic Unit
- Center for Precision and Automated Agricultural Systems
- Publisher
- Elsevier B.V
- Identifiers
- 99900547405601842
- Language
- English
- Resource Type
- Journal article