A Comparative Study of Deep Reinforcement Learning for Crop Production Management

• URL: http://arxiv.org/abs/2411.04106v1
• Date: Wed, 06 Nov 2024 18:35:51 GMT
• Title: A Comparative Study of Deep Reinforcement Learning for Crop Production Management
• Authors: Joseph Balderas, Dong Chen, Yanbo Huang, Li Wang, Ren-Cang Li,
• Abstract summary: 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.
• Score: 13.123171643387668
• License:
• Abstract: Crop production management is essential for optimizing yield and minimizing a field's environmental impact to crop fields, yet it remains challenging due to the complex and stochastic processes involved. Recently, researchers have turned to machine learning to address these complexities. Specifically, reinforcement learning (RL), a cutting-edge approach designed to learn optimal decision-making strategies through trial and error in dynamic environments, has emerged as a promising tool for developing adaptive crop management policies. RL models aim to optimize long-term rewards by continuously interacting with the environment, making them well-suited for tackling the uncertainties and variability inherent in crop management. Studies have shown that RL can generate crop management policies that compete with, and even outperform, expert-designed policies within simulation-based crop models. 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. However, these methods have not yet been systematically evaluated under identical conditions. 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. To ensure a fair comparison, we used consistent default parameters, identical reward functions, and the same environment settings. Our results indicate that PPO outperforms DQN in fertilization and irrigation tasks, while DQN excels in the mixed management task. This comparative analysis provides critical insights into the strengths and limitations of each approach, advancing the development of more effective RL-based crop management strategies.

Related papers

• Learning-based agricultural management in partially observable environments subject to climate variability [5.5062239803516615]
  Agricultural management holds a central role in shaping crop yield, economic profitability, and environmental sustainability. We introduce an innovative framework that integrates Deep Reinforcement Learning (DRL) with Recurrent Neural Networks (RNNs) Our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events.
  arXiv  Detail & Related papers  (2024-01-02T16:18:53Z)
• 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)
• Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning [64.10794426777493]
  Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks. Recent practices tend to distill optimized action sequences into an RL policy during the training phase. We develop an approach to distill from model-based planning to the policy.
  arXiv  Detail & Related papers  (2023-07-24T16:52:31Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Diverse Policy Optimization for Structured Action Space [59.361076277997704]
  We propose Diverse Policy Optimization (DPO) to model the policies in structured action space as the energy-based models (EBM) A novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies.
  arXiv  Detail & Related papers  (2023-02-23T10:48:09Z)
• 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)
• Optimizing Crop Management with Reinforcement Learning and Imitation Learning [9.69704937572711]
  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.
  arXiv  Detail & Related papers  (2022-09-20T20:48:52Z)
• Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty [8.427937898153779]
  We develop optimization frameworks under uncertainty using conditional value-at-risk in the objective programming function. As a case study, we set up the crop model for 25 locations across the US Corn Belt. Results indicated that the proposed model produced meaningful connections between weather and optima decisions.
  arXiv  Detail & Related papers  (2022-07-17T01:14:43Z)
• 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)
• Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning [83.66080019570461]
  We propose two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty. We show that these metrics have higher correlations with normalized task solvability scores than a variety of alternatives. These metrics can also be used for fast and compute-efficient optimizations of key design parameters.
  arXiv  Detail & Related papers  (2021-03-23T17:49:50Z)

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.