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Chlorophyll fluorescence sensing for early detection of crop’s diseases symptoms
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 021114, 2002 ASAE Annual Meeting . (doi: 10.13031/2013.10946) @2002
Authors: Luigi Bodria, Marco Fiala, Roberto Oberti, Ezio Naldi
Keywords: Disease detection, Chlorophyll fluorescence, Fluorescence imaging, Brown Rust, Yellow rust
A chlorophyll fluorescence imaging system based on a filtered xenon lamp, providing actinic light in
UV and violet bands, and on a high resolution camera equipped with a 690nm (FWHM=10nm) passband filter,
for single band measurements, and with a four bands beam splitter with pass-band filters (450nm, 550nm,
690nm, 740nm, all with a FWHM=10nm), for multispectral measurements, was implemented and applied on
wheat plants inoculated with different fungal infections, with the aim of investigating the potential of such a
technique for detecting plants disease symptoms.
In steady state fluorescence images of attached leaves acquired at 690nm in laboratory conditions, the
symptoms appear as highly emitting spots at sub-millimetric or millimetric scale which, with the progress of the
disease, develop in larger, low emitting lesions surrounded by high intensity halo. Even if the changes in
emission pattern are limited to the neighborhoods of the infection point; this technique allowed to detect
disease presence before visible symptoms appear.
Kinetic fluorescence imaging performed by acquiring a sequence of images at 690nm during an actinic
illumination period of several minutes, allowed to find differences between diseased and healthy areas, even at
very early stages, both in terms of intensity and time-dependence of emission. Nevertheless, the
excitation/sensing period of several minutes on which this technique is based, limits practical field applications
on moving vehicles.
Multispectral fluorescence imaging in field conditions resulted unsuccessful during day-time measurements due
to plants saturation by long-exposure to direct sunlight and to the interference of the diffuse environmental
illumination. On the contrary, night-time imaging confirmed the high potential of this technique for disease
detection and quantifications. In particular, an image analysis algorithm based on the ratio of fluorescence
images at 550nm and 690nm was implemented, allowing to discriminate plants lesions and to map the disease
severity in experimental plots in agreement with visual inspection made by a pathologist.
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