The aim of this thesis was to investigate the application of machine learning methods in plant genetics in order to enhance our understanding of the plant kingdom. By better understanding plants and complex traits such as the relationship between genotype and phenotype, we could address pressing environmental challenges in agronomy. I found that machine learning methods enable efficient processing of large amounts of data, increase the accuracy of phenotype prediction, and optimize existing technologies such as genome editing. Numerous studies have successfully applied machine learning methods to achieve their results, demonstrating the promising future of these technologies. Nevertheless, significant limitations remain, such as the lack of standardized data, limited interpretability of models, and challenges in transferring results from laboratory to natural conditions. The findings indicate that further progress requires closer integration of biological knowledge with computational approaches and the development of clearly interpretable models. For fully successful and effective application in agronomy, further research is necessary, especially consistent expansion of data repositories.
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