Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical se...

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Bibliographic Details
Published in:Precision agriculture 2010-08, Vol.11 (4), p.397-412
Main Authors: Polder, G, van der Heijden, G. W. A. M, van Doorn, J, Clevers, J. G. P. W, van der Schoor, R, Baltissen, A. H. M. C
Format: Article
Language:English
Subjects:
Agriculture
Analysis
Atmospheric Sciences
automatic detection
Biomedical and Life Sciences
Cameras
Chemistry and Earth Sciences
Chlorophyll
chlorophyll fluorescence
Computer Science
Crop diseases
Crops
Enzymes
Flowers & plants
Hyperspectral imaging
image analysis
Infections
information
Insecticides
Insects
leaf pigment content
Life Sciences
nitrogen
Pathogens
Physics
Plant diseases
Plant virus
plant-leaves
Precision farming
range
reflectance
Remote Sensing/Photogrammetry
Robots
Seasons
Sensors
shape
Soil Science & Conservation
spectroscopy
Spectrum analysis
Statistics for Engineering
Studies
Tulip breaking virus
Viral infections
Viruses
Vision systems
Wheat
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Summary:The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert.
ISSN:1385-2256
1573-1618
DOI:10.1007/s11119-010-9169-2