Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms
Paper
Paper/Presentation Title | Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms |
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Presentation Type | Paper |
Authors | Apan, Armando (Author), Datt, B. (Author) and Kelly, Rob (Author) |
Editors | Bellman, Chris, Cartwright, William and Shortis, Mark |
Journal or Proceedings Title | Proceedings of the 2005 Spatial Sciences Institute Biennial Conference 2005: Spatial Intelligence, Innovation and Praxis (SSC2005) |
Year | 2005 |
Place of Publication | Melbourne, Australia |
ISBN | 0958136629 |
Conference/Event | 2005 Spatial Sciences Institute Biennial Conference: Spatial Intelligence, Innovation and Praxis (SSC2005) |
Event Details | 2005 Spatial Sciences Institute Biennial Conference: Spatial Intelligence, Innovation and Praxis (SSC2005) Event Date 12 to end of 16 Sep 2005 Event Location Melbourne, Australia |
Abstract | The aim of the study was to examine the potential of hyperspectral sensing to detect the incidence of pests and diseases in vegetable crops. The specific objectives were: a) to test if symptoms of pests and diseases of vegetable crops can be detected by hyperspectral sensing, b) to determine the best spectral bands relevant to pest and disease detection, and c)to compare the spectral responses obtained from the symptoms of two different pest and disease. Using a handheld spectrometer, sample measurements of diseased/infested and healthy leaves were collected separately from tomato and eggplant crops. The tomato crops were affected by a fungal 'early blight' disease (Alternaria solani), with symptoms characterised by a yellowing or chlorosis of leaves. Conversely, the eggplants exhibited skeletal interveinal damage ('holes') on leaves, caused by the 28-spotted ladybird (Epilachna vigintioctopunctata). To overcome the problems of |
Keywords | hyperspectral sensing, pests and diseases, vegetables, PLS regression |
ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
300804. Horticultural crop protection (incl. pests, diseases and weeds) | |
Byline Affiliations | Faculty of Engineering and Surveying |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
Department of Primary Industries, Queensland |
https://research.usq.edu.au/item/9x94q/detection-of-pests-and-diseases-in-vegetable-crops-using-hyperspectral-sensing-a-comparison-of-reflectance-data-for-different-sets-of-symptoms
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