Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties

Zhaoan Wang; Shaoping Xiao; Jun Wang; Ashwin Parab; Shivam Patel

doi:10.48550/arxiv.2402.08832

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Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties

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Open access

Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties

Zhaoan Wang, Shaoping Xiao, Jun Wang, Ashwin Parab and Shivam Patel

ArXiv.org

Cornell University

02/13/2024

DOI: 10.48550/arxiv.2402.08832

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Computer Science - Artificial Intelligence

Computer Science - Computers and Society

Computer Science - Learning

Details

Title: Subtitle: Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties
Creators: Zhaoan Wang
Shaoping Xiao
Jun Wang
Ashwin Parab
Shivam Patel
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2402.08832
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 02/13/2024
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering; Mechanical Engineering
Record Identifier: 9984559878302771

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