Detecting Plant Diseases with Vegetation Indices

Agriculture today is not only challenged with the production of food and animal food, but also with aspects of environmental protection. In crop production, there is a growing pressure to decrease the utilization of pesticides, to lower potential production costs and reduce the environmental impact.

Therefore, it is crucial that pesticides are only used when and where necessary. If disease patches within fields could be identified and fungicides applied only to infected areas disease control might be more efficient.

Recent developments in optical sensor technologies have the potential to allow direct detection of foliar diseases under field conditions ([We03]). In this article, different vegetation indices were assessed for their potential to detect and identify different plant diseases.

In a number of field trials, the influence of Septoria tritici (leaf blotch disease) and Erysiphe graminis (powdery mildew) on canopy reflectance of winter wheat was examined. Canopy reflectance was calculated every seven days beginning in May until mid July depending on the development of the plants.

A field spectroradiometer Field Spec® Hand Held (ASD, Inc. Boulder, CO, USA) was used to perform Reflectance measurements. Reflectance data was assessed according to known vegetation indices in allegorized specified reflectance bands.

For the purpose of disease identification, vegetation indices were assessed for their suitability to detect differences in vitality between diseased and healthy plants and were utilized to compare the effectiveness of different sensor systems. Indices which could detect the relevant plant diseases were chosen from a multiplicity of existing vegetation indices.

The maximum yield of plants, established by their genetic potential, is not usually acquired. Elements like adverse climatic conditions, insufficient nutrients or water, plant diseases, and insect damage hinder growth at some point.

Plant diseases especially are one of the key reasons for loss in quality and yield. On an annual basis, around 30% of the world harvest is lost due to biotic stress factors ([HG00]).

Spraying pesticides uniformly over fields at different times during the cultivation cycle is still the technique that is most frequently utilized in pest and disease control in arable crops.

Yet, most disease infestations occur in patches and are not distributed evenly over the field. Pesticides should be targeted only in the locations of the field where they are required.

A cost-effective and simple optical device would enable disease patches to be identified and controlled [Mo05]. Multiple attempts utilizing multispectral sensors and satellite images in detection of diseased crops have been made ([Ka74], [PG77]).

In the last decade, vegetation indices which are based on simple combinations of near-infrared and visible reflectance, like the simple ratio (SR) and normalized difference vegetation index (NDVI), have been widely employed by the remote sensing community to monitor vegetation from space, both on global and regional scales.

The vegetation indices TCARI proposed by [Ha02], MCARI proposed by [Da00], OSAWI proposed by [RSB96] and the vegetation index NPCI proposed by [Pe94] were employed in this study for the analysis of the FieldSpec measurements.

References

[We03] West, J.S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D. and McCartney, H.A. 2003: The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology, Vol. 41:593-614.

[HG00] Habermeyer, J. and Gerhard, M. 2000. Pilzkrankheiten. BASF Landwirtschaft.

[Mo05] Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., and Ramon, H. 2005. Plant disease detection based on data fusion of hyper-spectral and multi –spectral fluorescence imaging using Kohonen maps. Real Time imaging 11: 75-83

[Ka74] Kanemasu, E.T., Niblett, C.L., Mangea, H., Lenhert, D., and Newman, M.A. 1974. Wheat: ist growth and disease severity as deduced from ERST-1. Remote sensing of Environment 3: 255-260.

[PG77] Pederson, V.D., and Gudmestad, N. 1977. Evaluation of foliar diseases of barley with multispectral sensors. Proc. American Phytopathology Society 4: 149.

[Da00] Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E., and Mc Murtrey III, J.E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74:229-239.

[Ha02] Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., and Dextraze, L. 2002. Integratuon of hyperspectral vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81: 416-426.

[Pe94] Penuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., and Field, C.B. 1994. Reflectance indices associated with physiological chances in nitrogen- and water-limited sunflower leafs. Remote Sensing of Environment 48: 135-146.

[LBD03] Laudien, R., Bareth, G. and Doluschitz, R. 2003. Analysis of hyperspectral field data for detection of sugar beet diseases. EFITA 2003 Conference Debrecen, Hungary: 375-381.

[Hu88] Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 295-309.

Acknowledgments

Produced from materials originally authored by Kerstin Gröll, Simone Graeff and Wilhelm Claupein from Institute for Crop Production and Grassland Research

This information has been sourced, reviewed and adapted from materials provided by Malvern Panalytical.

For more information on this source, please visit Malvern Panalytical.

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