Detection of soybean rust using a multispectral image sensor

被引：29

作者：

Cui D. ^{[1
]}

Zhang Q. ^{[2
]}

Li M. ^{[1
]}

Zhao Y. ^{[3
]}

Hartman G.L. ^{[3
,4
]}

机构：

[1] China Agricultural University

[2] Department of Agricultural and Biological Engineering, University of Illinois, Urbana-Champaign

[3] Department of Crop Sciences, University of Illinois, Urbana-Champaign

[4] USDA-ARS and Department of Crop Sciences, University of Illinois, Urbana-Champaign

来源：

Sensing and Instrumentation for Food Quality and Safety | 2009年 / 3卷 / 1期

关键词：

Disease area index; Infection level index; Leaf reflectance; Lesion color index; Multispectral image sensor; Soybean rust;

D O I：

10.1007/s11694-009-9070-8

中图分类号：

学科分类号：

摘要：

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. © Springer Science+Business Media, LLC 2009.

引用

页码：49 / 56

页数：7

共 25 条

[11]

Lorenzen B., Jensen A., Changes in spectral properties induced in Barley by cereal Powdery Mildew, Remote Sens. Environ., 27, 2, pp. 201-209, (1989)

[12]

Sasaki Y., Okamoto T., Imou K., Torii T., Automatic diagnosis of plant disease-Spectral reflectance of healthy and diseased leaves, Proc. of 3rd IFAC/CIGR Workshop on Artificial Intelligence in Agriculture, pp. 158-163, (1998)

[13]

Peterson R.B., Aylor D.E., Chlorophyll fluorescence induction in leaves of Phaseolus vulgaris infected with bean rust, Plant Physiol., 108, 1, pp. 163-171, (1995)

[14]

Ludeker W., Dahn H.G., Gunther K.P., Detection of fungal infection of plants by laser-induced fluorescence: An attempt to use remote sensing, J. Plant Physiol., 148, 5, pp. 579-585, (1996)

[15]

Johansson J., Andersson M., Edner H., Mattsson J., Svanberg S., Remote fluorescence measurements of vegetation spectrally resolved and by multi-colour fluorescence imaging, J. Plant Physiol., 148, 5, pp. 632-637, (1996)

[16]

Pinter Jr. P.J., Stanghellini M.E., Reginato R.J., Idso S.B., Jenkins A.D., Jackson R.D., Remote detection of biological stresses in plants with infrared thermometry, Science, 205, 4406, pp. 585-586, (1979)

[17]

Chaerle L., De Boever F., Van Montagu M., Van der Straeten D., Thermographic visualization of cell death in tobacco and Arabidopsis, Plant Cell Environ., 24, 1, pp. 15-25, (2001)

[18]

Bravo C., Moshou D., Oberti R., West J., McCartney A., Bodria L., Ramon H., Detection of Foliar Disease in the Field By the Fusion of Measurements Made By Optical Sensors, (2002)

[19]

Tucker C.J., Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 2, pp. 127-150, (1979)

[20]

Gitelson A.A., Merzlyak M.N., Lichtenthaler H.K., Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm, J. Plant Physiol., 148, 3-4, pp. 501-508, (1996)

← 1 2 3 →