Intelligent Agricultural Management Considering N$_2$O Emission and
  Climate Variability with Uncertainties
Zhaoan Wang Shaoping Xiao Jun Wang Ashwin Parab Shivam Patel
This study examines how artificial intelligence (AI), especially
Reinforcement Learning (RL), can be used in farming to boost crop yields,
fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse
gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate
change and limited agricultural knowledge, we use Partially Observable Markov
Decision Processes (POMDPs) with a crop simulator to model AI agents'
interactions with farming environments. We apply deep Q-learning with Recurrent
Neural Network (RNN)-based Q networks for training agents on optimal actions.
Also, we develop Machine Learning (ML) models to predict N$_2$O emissions,
integrating these predictions into the simulator. Our research tackles
uncertainties in N$_2$O emission estimates with a probabilistic ML approach and
climate variability through a stochastic weather model, offering a range of
emission outcomes to improve forecast reliability and decision-making. By
incorporating climate change effects, we enhance agents' climate adaptability,
aiming for resilient agricultural practices. Results show these agents can
align crop productivity with environmental concerns by penalizing N$_2$O
emissions, adapting effectively to climate shifts like warmer temperatures and
less rain. This strategy improves farm management under climate change,
highlighting AI's role in sustainable agriculture.