NAIFM - Nutrient biomarker for optimized AI–driven fertilizer management
Detection of nutrient biomarkers for AI-mediated fertilizer management in agricultural crop production in the field.
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Optimal nutrient supply with AI
The goal
The approach
To enable more precise, resource-saving fertilization in agriculture by recognizing where plants really need nutrients and reacting accordingly.
– Plants and soils are systematically examined (e.g. samples from plants and soil analyses).
– At the same time, drone images are created to capture characteristics of the plants that correlate with nutrient deficiency.
– The combination of image data, laboratory results and soil parameters will create a data-based, intelligent decision support system that enables pinpoint fertilization - i.e. only delivers fertilizer where it is needed.The analysis and demonstration will be carried out using nitrogen and winter rapeseed as examples.
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Acquisition of high-resolution image data
thermal DRONES is responsible for the collection and processing of image data of the plants in the fields - highest-resolution color, multispectral and thermal image data with which plant characteristics can be made visible. For example, the POICloud is used for high-precision flight planning and the necessary altitude compensation for flights at an altitude of 5 m.
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Illustration of the overall process
thermal DRONES provides the platform for carrying out the overall process from image data acquisition and analysis via artificial intelligence to the implementation of the fertilization process.
POIStudio and POICloud as well as hardware and software components specially developed for the project are used for this purpose.
Associated partners
WiR!-Bündnis, Stralsund University of Applied Sciences, ArtiFarm.
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Greifswald University Hospital
Prof. Dr. Stefan Simm
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Leipzig University
Dr. Vajiheh Safavi-Rizi
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thermal DRONES GmbH
Dr. Martin Israel
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Greifswalder Agrarinitiative e.V.
Thomas Beil