Intelligent Agricultural Management Considering N$_2$O Emission and Climate Variability with Uncertainties
This work addresses sustainable agriculture management for farmers and policymakers by providing AI-driven tools to mitigate greenhouse gas emissions under climate change, though it appears incremental as it builds on existing RL and ML methods applied to a specific domain.
This study tackled the problem of optimizing farming practices to balance crop yields with environmental impacts, specifically reducing Nitrous Oxide (N2O) emissions under climate variability, by using Reinforcement Learning agents integrated with crop simulators and probabilistic ML models, resulting in agents that effectively adapt to climate shifts and align productivity with emission penalties.
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.