Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-08T14:31:11.290Z Has data issue: false hasContentIssue false

Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée

Published online by Cambridge University Press:  01 June 2017

H. Al-Saddik*
Affiliation:
Agrosup Dijon, Joint Research Unit 1347 Agroecology, 26 Bd Dr Petitjean, 21000 Dijon, France
J.C. Simon
Affiliation:
Agrosup Dijon, Joint Research Unit 1347 Agroecology, 26 Bd Dr Petitjean, 21000 Dijon, France
O. Brousse
Affiliation:
GST (Global Sensing Technologies), Dijon, France
F. Cointault
Affiliation:
Agrosup Dijon, Joint Research Unit 1347 Agroecology, 26 Bd Dr Petitjean, 21000 Dijon, France
*
Get access

Abstract

Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.

Type
Crop Sensors and Sensing
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Araujo, M, Kawakami, T, Galvao, R, Yoneyama, T, Chame, H and Visani, V 2001. The Succesive Projection Algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems 6573.CrossRefGoogle Scholar
Chuche, J and Thiéry, D 2014. Biology and ecology of the Flavescence dorée vector Scaphoideus titanus: a review. Agronometry for Sustainble Development 34, 381403.CrossRefGoogle Scholar
Hall, A, Lamb, D, Holzapfel, B and Louis, J 2002. Optical remote sensing in viticulture-a review. Australian journal of grape and wine research 8, 3647.Google Scholar
Hou, J, Li, L and He, J 2016. Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm. Precision Agriculture 17, 488505.Google Scholar
MacDonald, S, Staid, M and Cooper, M 2016. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards. Computers and Electronics in Agriculture 130, 109117.Google Scholar
Naidu, R, Perry, E, Pierce, F and Mekuria, T 2009. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture 66, 3845.Google Scholar
Oberti, R, Marchi, M, Tirelli, P, Calcante, A, Iriti, M and Borghese, A 2014. Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity. Computers and Electronics in Agriculture 104, 18.CrossRefGoogle Scholar
Rinnan, A, Berg, F and Engelsen, S 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry 28, 12011222.Google Scholar
Whalley, S and Shanmuganathan, J 2013. Applications of image processing in viticulture: A review. 20th International Congress on Modelling and Simulation. Adelaide, Australia 531537.Google Scholar
Yang, X, Hong, H, You, Z and Cheng, F 2015. Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification. Sensors 1557815594.Google Scholar
Zhang, Y, Tan, L, Shi, H and He, Y 2013. Successive Projections Algorithm for Variable Selection on the Rapid and Non-Destructive Classification of Coolant. International Journal of Digital Content Technology and its Applications 7, 386394.Google Scholar