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Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
What is Climate?01:16

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.

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Related Experiment Videos

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
PubMed
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.

Keywords:
Climate variabilityDecision-makingFertilization managementPartially observable environmentsRecurrent neural networksReinforcement learning

Related Experiment Videos

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.