Optimizing Crop Management with Reinforcement Learning and Imitation Learning

• URL: http://arxiv.org/abs/2209.09991v1
• Date: Tue, 20 Sep 2022 20:48:52 GMT
• Title: Optimizing Crop Management with Reinforcement Learning and Imitation Learning
• Authors: Ran Tao, Pan Zhao, Jing Wu, Nicolas F. Martin, Matthew T. Harrison, Carla Ferreira, Zahra Kalantari, Naira Hovakimyan
• Abstract summary: We present an intelligent crop management system which optimize the N fertilization and irrigation simultaneously via reinforcement learning (RL), imitation learning (IL), and crop simulations. We conduct experiments on a case study using maize in Florida and compare trained policies with a maize management guideline in simulations. Our trained policies under both full and partial observations achieve better outcomes, resulting in a higher profit or a similar profit with a smaller environmental impact.
• Score: 9.69704937572711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crop management, including nitrogen (N) fertilization and irrigation management, has a significant impact on the crop yield, economic profit, and the environment. Although management guidelines exist, it is challenging to find the optimal management practices given a specific planting environment and a crop. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system which optimizes the N fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require all state information from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a limited amount of state information that can be readily obtained in the real world (denoted as partial observation) by mimicking the actions of the previously RL-trained policies under full observation. We conduct experiments on a case study using maize in Florida and compare trained policies with a maize management guideline in simulations. Our trained policies under both full and partial observations achieve better outcomes, resulting in a higher profit or a similar profit with a smaller environmental impact. Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.

Related papers

• A Comparative Study of Deep Reinforcement Learning for Crop Production Management [13.123171643387668]
  Reinforcement learning (RL) has emerged as a promising tool for developing adaptive crop management policies. In the gym-DSSAT crop model environment, one of the most widely used simulators for crop management, proximal policy optimization (PPO) and deep Q-networks (DQN) have shown promising results. In this study, we evaluated PPO and DQN against static baseline policies across three different RL tasks, fertilization, irrigation, and mixed management, provided by the gym-DSSAT environment.
  arXiv  Detail & Related papers  (2024-11-06T18:35:51Z)
• AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning [8.205412609306713]
  We present AgGym, a modular, crop and stress simulation framework to model the spread of biotic stresses in a field. We show that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. Our proposed framework enables personalized decision support that can transform biotic stress management from being schedule based to opportunistic and prescriptive.
  arXiv  Detail & Related papers  (2024-09-01T14:55:45Z)
• The New Agronomists: Language Models are Experts in Crop Management [11.239822736512929]
  This paper introduces a more advanced intelligent crop management system. We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model's capacity to understand states and explore optimal management practices.
  arXiv  Detail & Related papers  (2024-03-28T21:20:27Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• A SWAT-based Reinforcement Learning Framework for Crop Management [0.0]
  We introduce a reinforcement learning (RL) environment that leverages the dynamics in the Soil and Water Assessment Tool (SWAT) This drastically saves time and resources that would have been otherwise deployed during a full-growing season. We demonstrate the utility of our framework by developing and benchmarking various decision-making agents following management strategies informed by standard farming practices and state-of-the-art RL algorithms.
  arXiv  Detail & Related papers  (2023-02-10T00:24:22Z)
• Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learning [78.31888150539258]
  Reinforcement learning (RL) agents have long sought to approach the efficiency of human learning. Prior studies in RL have incorporated external knowledge policies to help agents improve sample efficiency. We present Knowledge-Grounded RL (KGRL), an RL paradigm fusing multiple knowledge policies and aiming for human-like efficiency and flexibility.
  arXiv  Detail & Related papers  (2022-10-07T17:56:57Z)
• Exploration via Planning for Information about the Optimal Trajectory [67.33886176127578]
  We develop a method that allows us to plan for exploration while taking the task and the current knowledge into account. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines.
  arXiv  Detail & Related papers  (2022-10-06T20:28:55Z)
• Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations [11.576438685465797]
  Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT) We then train management policies with deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface that allows for daily interactions between the simulated crop environment and RL agents.
  arXiv  Detail & Related papers  (2022-04-21T20:26:41Z)
• Curriculum Offline Imitation Learning [72.1015201041391]
  offline reinforcement learning tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. We propose textitCurriculum Offline Learning (COIL), which utilizes an experience picking strategy for imitating from adaptive neighboring policies with a higher return. On continuous control benchmarks, we compare COIL against both imitation-based and RL-based methods, showing that it not only avoids just learning a mediocre behavior on mixed datasets but is also even competitive with state-of-the-art offline RL methods.
  arXiv  Detail & Related papers  (2021-11-03T08:02:48Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.