|Cui, Di - University Of Illinois|
|Zhang, Q - University Of Illinois|
|Li, M - University Of Illinois|
|Zhao, Y - University Of Illinois|
Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/8/2010
Publication Date: 8/21/2010
Publication URL: http://hdl.handle.net/10113/47561
Citation: Cui, D., Zhang, Q., Li, M., Zhao, Y., Hartman, G.L. 2010. Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosystems Engineering. 107:186-193.
Interpretive Summary: Soybean rust, caused by the fungal pathogen Phakopsora pachyrhizi, is one of the most destructive foliar disease for soybean. In order to implement timely fungicide treatments for the most effective control of the disease, it is essential to detect early infection of soybean rust. In this study, images of soybean leaves with different rust severities were collected using both a portable spectroradiometer and a multispectral camera. Results indicated that both leaf development stage and rust infection severity changed the surface reflectance making this methodology potentially useful for early diagnosis. This information is important to soybean diagnosticians and crop consultants that may use this methodology for early detection of soybean rust.
Technical Abstract: Soybean rust, caused by Phakopsora pachyrhizi, is one of the most destructive diseases for soybean production. It often causes significant yield loss and may rapidly spread from field to field through airborne urediniospores. In order to implement timely fungicide treatments for the most effective control of the disease, it is essential to detect the infection and severity of soybean rust. This research explored feasible methods for detecting soybean rust and quantifying severity. In this study, images of soybean leaves with different rust severity were collected using both a portable spectroradiometer and a multispectral CDD camera. Different forms of vegetation indices were used to investigate the possibility of detecting rust infection. Results indicated that both leaf development stage and rust infection severity changed the surface reflectance within a wide band of spectrum. In general, old leaves with most severe rust infection resulted in lowest reflectance. A difference vegetation index (DVI) showed a positive correlation with reflectance differences. However, it lacks solid evidence to identify such reflectance change was solely caused by rust. As an alternative, three parameters, i.e. ratio of infected area (RIA), lesion color index (LCI) and rust severity index (RSI), were extracted from the multispectral images and used to detect leaf infection and severity of infection. The preliminary results obtained from this laboratory-scale research demonstrated that this multispectral imaging method could quantitatively detect soybean rust. Further tests of field scale are needed to verify the effectiveness and reliability of this sensing method to detect and quantify soybean rust infection in real time field scouting.