Next Article in Journal
A Real-Time Robust Method to Detect BeiDou GEO/IGSO Orbital Maneuvers
Next Article in Special Issue
A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field
Previous Article in Journal
GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force
Previous Article in Special Issue
Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2772; https://doi.org/10.3390/s17122772

Development of Spectral Disease Indices for ‘Flavescence Dorée’ Grapevine Disease Identification

INRA, UMR 1347 Agroecology, 21000 Dijon, France
*
Author to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 24 November 2017 / Accepted: 26 November 2017 / Published: 29 November 2017
(This article belongs to the Special Issue Sensors in Agriculture)
View Full-Text   |   Download PDF [3091 KB, uploaded 30 November 2017]   |  

Abstract

Spectral measurements are employed in many precision agriculture applications, due to their ability to monitor the vegetation’s health state. Spectral vegetation indices are one of the main techniques currently used in remote sensing activities, since they are related to biophysical and biochemical crop variables. Moreover, they have been evaluated in some studies as potentially beneficial for detecting or differentiating crop diseases. Flavescence Dorée (FD) is an infectious, incurable disease of the grapevine that can produce severe yield losses and, hence, compromise the stability of the vineyards. The aim of this study was to develop specific spectral disease indices (SDIs) for the detection of FD disease in grapevines. Spectral signatures of healthy and diseased grapevine leaves were measured with a non-imaging spectro-radiometer at two infection severity levels. The most discriminating wavelengths were selected by a genetic algorithm (GA) feature selection tool, the Spectral Disease Indices (SDIs) are designed by exhaustively testing all possible combinations of wavelengths chosen. The best weighted combination of a single wavelength and a normalized difference is chosen to create the index. The SDIs are tested for their ability to differentiate healthy from diseased vine leaves and they are compared to some common set of Spectral Vegetation Indices (SVIs). It was demonstrated that using vegetation indices was, in general, better than using complete spectral data and that SDIs specifically designed for FD performed better than traditional SVIs in most of cases. The precision of the classification is higher than 90%. This study demonstrates that SDIs have the potential to improve disease detection, identification and monitoring in precision agriculture applications. View Full-Text
Keywords: spectral analysis; feature selection; genetic algorithms; classification; vegetation indices; vineyard; diseases spectral analysis; feature selection; genetic algorithms; classification; vegetation indices; vineyard; diseases
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

AL-Saddik, H.; Simon, J.-C.; Cointault, F. Development of Spectral Disease Indices for ‘Flavescence Dorée’ Grapevine Disease Identification. Sensors 2017, 17, 2772.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top