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Application of soil- and airborne-based sensor methods for virus detection in seedling production of starch potatoes
Project
Project code: 22015218
Contract period: 16.10.2019
- 15.10.2022
Budget: 284,895 Euro
Purpose of research: Applied research
Keywords: digitalization
In the production of starch potatoes, viruses cause high annual yield losses. Due to the vegetative propagation, unrecognized virus infestation is passed on directly to the next propagation stage and then causes great economic damage to plant propagation enterprises. The most effective means avoiding virus infestation in seed potato propagation is therefore the early cleanup of propagating stocks, i.e. the removal of sick plants in the field, which are a potential source of infection. As example, the potato Y virus (PVY = Potato Virus Y) is to be mentioned as a representative of a whole series of viruses that have to be identified and selected in the field. The personnel expenditure of trained persons is very high, experts are due to the shrinking offer in many regions no longer available. The goal of the project is thus the automated detection of virus-infected plants in field crops with a modular imaging sensor system. It consists of innovative compact hyperspectral imaging sensors (XIMEA) on ground and airborne platforms; a new sensor technology developed by the applicants - "multi-wavelength laser line profile system" (MWLP) - combines spectral signatures with 3D technology and is considered to be a promising technology for virus detection. At the same time, classic color cameras are used on ground- and air-based platforms to test the feasibility and quality of "low cost" solutions. For the analysis of data and field tests, classical methods of image processing and statistics as well as methods of machine learning are used. Spatially high-resolution information on the virus infestation creates the potential for selective actuator systems for regulation and thus combines ecological and economic goals.
Section overview
Subjects
- Crop Production
- Crop Protection
- Computer science
