Publication type: Conference poster
Type of review: Not specified
Title: Budget remote phenotyping : potential and limitations of consumer-grade NIRGB and red edge cameras for early detection of plant leaf diseases
Authors: Fahrentrapp, Johannes
Geilhausen, Martin
Laube, Patrick
Conference details: 2nd General Meeting of COST Action FA 1306, Copenhagen, Denmark, 18-20 April 2016
Issue Date: 18-Apr-2016
Language: English
Subjects: IUNR; Wein
Subject (DDC): 630: Agriculture
Abstract: Plant leaf diseases can be very destructive and for many crops they lead to lower fruit quality and yield. To safe our harvests and income many preventive pesticides treatments are applied to manage diseases. Early detection could help to substantially reduce pesticide application to healthy crops allowing a site-specific treatment. Diseases in leaves can be detected and specified using hyperspectral images. Some approaches even allow the detection before eye-visible symptoms appear. These imagers are expensive, usually not very handy, and typically not constructed for field use. We analyzed series of images of Phytophthora infestans and Botrytis cinerea artificially inoculated tomato leaves within their first 48 hours of incubation. Images were taken with two different handheld, low cost Canon Power Shot cameras producing three band JPEGs (near-infra-red, green, blue, and red-edge, green, blue). At 24 hours past inoculation (hpi) first differences could be detected in single band reflectance between healthy (mock-inoculated) and pathogen-inoculated leaf disks. Significant differences could be found in reflectance range, mean and median values as well as in the index pigment specific simple ratio (PSSRb). Current findings will be presented.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Natural Resource Sciences (IUNR)
Published as part of the ZHAW project: Multispektrale Bildgebung: Der Schl├╝ssel zur Fr├╝herkennung von Blattkrankheiten
Appears in collections:Publikationen Life Sciences und Facility Management

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