Abstract
Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision spaces, long-term objectives, and the need to adapt to rapid climate change. This paper introduces the use of Reinforcement Learning (RL) to optimize simulated crop breeding programs. RL agents are trained to make optimal crop selection and cross-breeding decisions based on genetic information. To benchmark RL-based breeding algorithms, we introduce a suite of Gym environments. The study demonstrates the superiority of RL techniques over standard practices in terms of genetic gain when simulated in silico using real-world genomic maize data. Show more
Publication status
publishedExternal links
Book title
NeurIPS 2023 Workshop on Tackling Climate Change with Machine LearningPages / Article No.
Publisher
Climate Change AIEvent
Organisational unit
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
03908 - Krause, Andreas / Krause, Andreas
Notes
Poster presented on December 16, 2023.More
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ETH Bibliography
yes
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