Machine Learning aided Crop Yield Optimization
This work addresses global food demands due to population growth and climate change by proposing a hybrid plant modeling and data-driven approach for crop yield optimization.
The authors tackled crop yield optimization by developing a crop simulation environment with an OpenAI Gym interface and applying deep reinforcement learning (DRL) algorithms to discover policies that optimize yield while minimizing water and fertilizer usage, showing that DRL can be useful for this purpose.
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL algorithms may be useful in discovering new policies and approaches to help optimize crop yield, while simultaneously minimizing constraining factors such as water and fertilizer usage. We propose that this hybrid plant modeling and data-driven approach for discovering new strategies to optimize crop yield may help address upcoming global food demands due to population expansion and climate change.