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Image processing methods for quantitatively detecting soybean rust from multispectral images

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Soybean rust is one of the most destructive foliar diseases of soybean, and can cause significant yield loss. Timely application of fungicide in the early stage of rust infection, which is critically important for effective control of the disease, is heavily dependent upon the capability to quantitatively detect the infection. This paper reports research outcomes from developing image processing methods for quantitatively detecting rust severity from multi-spectral images. A fast manual threshold-setting method was originally developed based on HSI (Hue Saturation Intensity) colour model for segmenting infected areas from plant leaves. Two disease diagnostic parameters, ratio of infected area (RIA) and rust colour index (RCI), were extracted and used as symptom indicators for quantifying rust severity. To achieve automatic rust detection, an alternative method of analysing the centroid of leaf colour distribution in the polar coordinate system was investigated. Leaf images with various levels of rust severity were collected and analyzed. Validation results showed that the threshold-setting method was capable of detecting soybean rust severity under laboratory conditions, whereas the centroid-locating method had the potential to be applied in the field.
Cui, Di , Zhang, Qin , Li, Minzan , Hartman, Glen L. , Zhao, Youfu
image analysis , quantitative analysis , disease detection , Phakopsora pachyrhizi , rust diseases , multispectral imagery , disease control
p. 186-193.
Includes references
Biosystems engineering 2010 Nov., v. 107, issue 3
Journal Articles, USDA Authors, Peer-Reviewed
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