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ARS Home » Midwest Area » Urbana, Illinois » Soybean/maize Germplasm, Pathology, and Genetics Research » Research » Publications at this Location » Publication #242620

Title: Detecting soybean rust severity in terms of multispectral images

Author
item Cui, Di - China Agricultural University
item Minzan, Li - China Agricultural University
item Zhang, Qin - University Of Illinois
item Hartman, Glen
item Zhao, Youfu - University Of Illinois

Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: 8/22/2009
Publication Date: 8/22/2009
Citation: Cui, D., Minzan, L., Zhang, Q., Hartman, G.L., Zhao, Y. 2009. Detecting soybean rust severity in terms of multispectral images [abstract]. In: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE). June 21-24, 2009, Reno, NV. 2009 CD ROM.

Interpretive Summary:

Technical Abstract: Soybean rust is one of the most destructive foliar diseases of soybean primarily because it produces copious amounts of airborne spores that can infect large areas of soybean production causing significant yield losses if left unchecked. Timely application of fungicide in the early stage of rust infection is critical for effective control of the disease, and heavily relies on the capability of detecting the degree of infection or severity. This paper reported research outcomes from developing an image processing method for quantitatively detecting rust severity from multispectral images. A simpler and faster threshold tuning method was developed based on HSI (Hue Saturation Intensity) color model for segmenting disease infected area from plant leaves. Two disease diagnostic parameters, i.e. ratio of infected area (RIA) and rust severity index (RSI), were extracted and used as symptom indicators for quantifying rust severity. To realize timely and automatic rust detection, another method of analyzing the centroid of leaf color distribution in the polar coordinate system was investigated to replace the segmentation approach. Plant images with various levels of rust severity were collected to support this research. Test results proved that the segmentation method was capable of detecting degrees of soybean rust severity under laboratory conditions by calculating RIA and RSI. The centroid locating method had a potential to be used for practical application in the field.