Intelligent Agricultural Management Considering N$_2$O Emission and Climate Variability with Uncertainties

TL;DR

This paper presents an AI-driven approach using reinforcement learning and probabilistic modeling to optimize agricultural practices, reduce N$_2$O emissions, and adapt to climate variability, enhancing sustainable farming under climate change.

Contribution

It introduces a novel integration of deep RL with ML-based emission prediction and climate variability modeling for resilient, environmentally conscious agricultural management.

Findings

AI agents effectively reduce N$_2$O emissions while maintaining crop yields.

Incorporating climate variability improves the robustness of agricultural decisions.

The approach demonstrates adaptability to climate change scenarios.

Abstract

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.