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Detection of soybean rust using a multispectral image sensor

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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.

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Acknowledgements

This research was partially supported by USDA Hatch Funds (ILLU-10-352 AE) and Bruce Cowgur Mid-Tech Memorial Funds. The State Scholarship Fund of China provided a scholarship fund to support Ms. Di Cui in conducting her doctoral thesis research at the University of Illinois at Urbana-Champaign. Any opinions, findings, and conclusions expressed in this publication are those of the authors and do not necessarily reflect the views of USDA, the University of Illinois, China Agricultural University, and Ministry of Education of the People’s Republic of China. Trade and manufacturer’s names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by USDA implies no approval of the product to the exclusion of others that may also be suitable.

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Authors and Affiliations

  1. China Agricultural University, Beijing, 100083, People’s Republic of China

    Di Cui & Minzan Li

  2. Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Qin Zhang

  3. Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Youfu Zhao

  4. USDA-ARS and Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Glen L. Hartman

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  1. Di Cui
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  2. Qin Zhang
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  3. Minzan Li
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  5. Glen L. Hartman
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Correspondence to Qin Zhang.

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Cui, D., Zhang, Q., Li, M. et al. Detection of soybean rust using a multispectral image sensor. Sens. & Instrumen. Food Qual. 3, 49–56 (2009). https://doi.org/10.1007/s11694-009-9070-8

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  • DOI: https://doi.org/10.1007/s11694-009-9070-8

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