Detecting soybean rust severity in terms of multispectral images

Di Cui, Minzan Li, Qin Zhang, Glen L. Hartman, Youfu Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Soybean rust is one of the most destructive foliar diseases of soybean primarily because it produces copious amounts of air-borne 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.

Original languageEnglish (US)
Title of host publicationAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009
Pages1327-1342
Number of pages16
StatePublished - Dec 1 2009
EventAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2009 - Reno, NV, United States
Duration: Jun 21 2009Jun 24 2009

Publication series

NameAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009
Volume2

Other

OtherAmerican Society of Agricultural and Biological Engineers Annual International Meeting 2009
CountryUnited States
CityReno, NV
Period6/21/096/24/09

Fingerprint

soybean rust
Soybeans
soybeans
rust diseases
Color
research support
color
disease models
foliar diseases
processing technology
methodology
Plant Leaves
signs and symptoms (plants)
pesticide application
leaves
disease control
Infection
Spores
spores
image analysis

Keywords

  • Centroid
  • Disease area index
  • Hue saturation intensity
  • Infection level index
  • Multispectral imaging sensor
  • Soybean rust

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)

Cite this

Cui, D., Li, M., Zhang, Q., Hartman, G. L., & Zhao, Y. (2009). Detecting soybean rust severity in terms of multispectral images. In American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009 (pp. 1327-1342). (American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009; Vol. 2).

Detecting soybean rust severity in terms of multispectral images. / Cui, Di; Li, Minzan; Zhang, Qin; Hartman, Glen L.; Zhao, Youfu.

American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009. 2009. p. 1327-1342 (American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cui, D, Li, M, Zhang, Q, Hartman, GL & Zhao, Y 2009, Detecting soybean rust severity in terms of multispectral images. in American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009. American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009, vol. 2, pp. 1327-1342, American Society of Agricultural and Biological Engineers Annual International Meeting 2009, Reno, NV, United States, 6/21/09.
Cui D, Li M, Zhang Q, Hartman GL, Zhao Y. Detecting soybean rust severity in terms of multispectral images. In American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009. 2009. p. 1327-1342. (American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009).
Cui, Di ; Li, Minzan ; Zhang, Qin ; Hartman, Glen L. ; Zhao, Youfu. / Detecting soybean rust severity in terms of multispectral images. American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009. 2009. pp. 1327-1342 (American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009).
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