Annual Report 2021

Page 67

Sponsor:

Bayerische Landesanstalt für Landwirtschaft, Institut für Pflanzenbau und Pflanzenzüchtung (Bavarian State Research Center for Agriculture, Institute for Plant Production and Plant Breeding)

Financing:

Förderung aus Mitteln der Gesellschaft für Hopfenforschung (GfH) (Support from the Society for Hop Research)

Project Management: Team: Collaboration: Duration:

S. Euringer K. Lutz, F. Weiß, Team IPZ 5b geo-konzept GmbH May 2021 to August 2021

Objective As a result of the progressive digitization in agriculture, data is increasingly being generated using remote sensing by UAVs (unmanned aircraft vehicles; colloquially known as drones). This technique is already in use for the early detection of pests or diseases and for the assessment of new varieties of several arable crops. Recently, there have also been advances in remote sensing as a form of non-destructive measurement of the N supply status of plants, as well as for estimates of biomass growth for optimal plant nutrition. Insights into the remote sensing of other arable crops should be transferred to hops. Specifically, such data might provide indications of Verticillium infestations before the appearance of visible symptoms. Method Remote sensing data was recorded with UAV flights. In addition to using different sensors for recording wavelengths in the visible spectrum, drones are also able to use different sensors for wavelengths that are not visible to the human eye. Such multi- and hyperspectral images are of particular interest to science because they can produce indicators of plant stress, for instance. The validation and classification of such digitally generated data, however, requires a large number of subsequent analogue assessments of the same growth areas. Only a combination of both drone and empirical observations can generate reliable data. Hyperspectral varietal differences as features in breeding new hop varieties With the help of hyperspectral recordings, the reflection of quanta outside the spectrum of visible light can be measured and gathered as objective, numerical values. These are complementary to the visual data generated by visual assessments of plant health and nutrition.

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