Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

Anne-Katrin Mahlein; Christian Hillnhütter; Thorsten Mewes; Christine Scholz; Ulrike Steiner; Heinz-Willhelm Dehne; Erich-Christian Oerke

doi:10.1117/12.830455

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18 September 2009 Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

Anne-Katrin Mahlein, Christian Hillnhütter, Thorsten Mewes, Christine Scholz, Ulrike Steiner, Heinz-Willhelm Dehne, Erich-Christian Oerke

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Proceedings Volume 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI; 747228 (2009) https://doi.org/10.1117/12.830455
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.

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Anne-Katrin Mahlein, Christian Hillnhütter, Thorsten Mewes, Christine Scholz, Ulrike Steiner, Heinz-Willhelm Dehne, and Erich-Christian Oerke "Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales", Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 747228 (18 September 2009); https://doi.org/10.1117/12.830455

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