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Learning-based agricultural management in partially observable environments subject to climate variability.
Zhaoan Wang1, Shaoping Xiao2, Junchao Li1
1Department of Mechanical Engineering, Iowa Technology Institute, University of Iowa, 3131 Seamans Center, Iowa City, Iowa, 52242, USA.
Scientific Reports
|June 23, 2026
Summary
This study uses Deep Reinforcement Learning (DRL) and Recurrent Neural Networks (RNNs) to optimize nitrogen fertilization for corn crops. The AI agent adapts strategies to climate variability, improving yields and sustainability.
Area of Science:
- Agricultural Science
- Artificial Intelligence
- Climate Science
Background:
- Conventional fertilization guidelines are challenged by extreme weather events like heatwaves and droughts.
- Optimizing nitrogen fertilization is crucial for crop yield, economic viability, and environmental sustainability.
Purpose of the Study:
- To develop an innovative framework integrating Deep Reinforcement Learning (DRL) and Recurrent Neural Networks (RNNs) for optimal nitrogen fertilization management.
- To evaluate the performance of Partially Observable Markov Decision Process (POMDP) and Markov Decision Process (MDP) models in agricultural simulations.
- To assess the impact of climate variability and extreme weather on agricultural outcomes and management strategies.
Main Methods:
- Utilized the Gym-DSSAT simulator to train an intelligent agent for nitrogen fertilization management.
- Conducted simulation experiments on corn crops in Iowa, comparing POMDP and MDP models.
- Analyzed the effectiveness of sequential observations in developing efficient nitrogen input strategies.
Main Results:
- The DRL-RNN framework demonstrated adaptability of fertilization strategies to varying climate conditions.
- A fixed fertilization strategy showed resilience to minor climate fluctuations, yielding good results.
- Extreme weather events necessitate agent retraining to acquire new optimal strategies.
Conclusions:
- Sequential observations enhance the development of efficient nitrogen input strategies.
- Adaptable fertilization strategies are essential for optimizing crop management in dynamic climate scenarios.
- This research paves the way for AI-driven agricultural practices resilient to climate change.
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