Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 6 (2004): C. Bravo, D. Moshou, R. Oberti, J. West, A. McCartney, L. Bodria and H. Ramon. Foliar Disease Detection in the Field Using Optical Sensor Fusion. (December 2004)

Bodria, L.

Bravo, C.

McCartney, A.

Moshou, D.

Oberti, R.

Ramon, H.

West, J.

The objective of this research was to detect and recognize the plant stress caused by disease in the
field conditions by combining hyperspectral reflection information between 450 and 900nm and
fluorescence imaging. The results can be used to develop a tractor mounted cost-effective optical
device for site-specific pesticide application in order to reduce and optimize pesticide use. The work
reported here used yellow rust (Puccinia striiformis) disease of winter wheat as a model system. In the
field hyperspectral reflection images of healthy and infected plants were taken by an imaging
spectrograph mounted at spray boom height. Leaf recognition and spectral normalization procedures
were used to account for differences in canopy architecture and spectral illumination were used. A
model, based on quadratic discrimination, was built, using a selected group of wavebands to
differentiate diseased from healthy plants. The model could discriminate diseased from healthy crop
with an error of about 10% using measurements from only three wavebands. Multispectral
fluorescence images were taken on the same plants using UV-blue excitation. Through comparison of
the 550 and 690 nm fluorescence images, the detection of disease was clearly possible. Fraction of
pixels in one image, recognized as diseased, was set as final fluorescence disease variable and called
the lesion index (LI). The lesion index was added to the pool of normalized selected reflection
wavebands. This pool of observations was used in a quadratic discrimination model. The combined
model improved disease discrimination compared to either the spectral model or fluorescent model
and had a classification error of between 5 and 6 %.
The results suggest that there is a potential for developing detection systems based on multisensor
measurements that can be used in precision disease control systems for use in arable crops

Foliar Disease Detection in the Field Using Optical Sensor Fusion

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