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Collaborative project: Breeding of locally-adapted varieties using artificial intelligence algorithms - subproject B (KIBREED)
Project
Project code: 28DK131B20
Contract period: 01.06.2021
- 31.05.2024
Budget: 200,810 Euro
Purpose of research: Applied research
Keywords: wheat, forecast, abiotic stress, crop production, digital world, plant genetic resources, AI Artificial Intelligence, agricultural biodiversity, biological diversity
The use of artificial intelligence in plant breeding has been very limited so far. However, a data-based application of artificial intelligence holds enormous potential for breeding. It can drive the development of resistant and locally adapted varieties, thereby strengthening agriculture, health nutrition and rural areas, as well as improving efficiency, sustainability and ecology in agriculture. The aim of the KIBREED research project is to make artificial intelligence algorithms usable for breeding locally adapted varieties. By means of "Deep Learning" procedures, data from the methods of "Genotyping", "Phenotyping" and "Envirotyping" are to be analyzed in an integrated way. The project objectives are to be explored using wheat as a model crop, since wheat is one of the world's most important crops. With the plant breeding company KWS and the Leibniz Institute of Plant Genetics and Crop Plant Research, the project brings together partners from industry and science who have unique and complementary expertise in applied and predictive breeding worldwide. KWS' broad range of seed products guarantees transfer of the research findings to other crops.
Section overview
Subjects
- Plant Breeding
