Breeding Programs Optimization with Reinforcement Learning
This addresses the challenge of improving agricultural productivity with long-term, high-dimensional breeding decisions, though it is incremental as it applies existing RL methods to a new domain.
The paper tackled optimizing crop breeding programs by applying Reinforcement Learning to make selection and cross-breeding decisions, demonstrating RL's superiority over standard practices in genetic gain using simulated maize genomic data.
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.