Digital Twins have gained attention in various industries for simulation,
monitoring, and decision-making, relying on ever-improving machine learning
models. However, agricultural Digital Twin implementations are limited compared
to other industries. Meanwhile, machine learning, particularly reinforcement
learning, has shown potential in agricultural applications like optimizing
decision-making, task automation, and resource management. A key aspect of
Digital Twins is representing physical assets or systems in a virtual
environment, which aligns well with reinforcement learning's need for
environment representations to learn the best policy for a task. Reinforcement
learning in agriculture can thus enable various Digital Twin applications in
agricultural domains. This review aims to categorize existing research
employing reinforcement learning in agricultural settings by application
domains like robotics, greenhouse management, irrigation systems, and crop
management, identifying potential future areas for reinforcement learning-based
Digital Twins. It also categorizes the reinforcement learning techniques used,
including tabular methods, Deep Q-Networks (DQN), Policy Gradient methods, and
Actor-Critic algorithms, to overview currently employed models. The review
seeks to provide insights into the state-of-the-art in integrating Digital
Twins and reinforcement learning in agriculture, identifying gaps and
opportunities for future research, and exploring synergies to tackle
agricultural challenges and optimize farming, paving the way for more efficient
and sustainable farming methodologies.

数字孪生在农业中的应用研究综述了以强化学习为基础的数字孪生在农业领域的应用，包括机器人技术、温室管理、灌溉系统和作物管理等，旨在发掘数字孪生与强化学习的整合研究现状，确定未来研究的机会和挑战，并探索协同解决农业问题和优化农业经营的方式，为更高效和可持续的农业方法铺平道路。

农业中基于强化学习的数字孪生当前应用与未来潜力

Current applications and potential future directions of reinforcement  learning-based Digital Twins in agriculture

Topological deep learning (TDL) is a rapidly evolving field that uses
topological features to understand and design deep learning models. This paper
posits that TDL may complement graph representation learning and geometric deep
learning by incorporating topological concepts, and can thus provide a natural
choice for various machine learning settings. To this end, this paper discusses
open problems in TDL, ranging from practical benefits to theoretical
foundations. For each problem, it outlines potential solutions and future
research opportunities. At the same time, this paper serves as an invitation to
the scientific community to actively participate in TDL research to unlock the
potential of this emerging field.

拓扑深度学习是一个快速发展的领域，它使用拓扑特征来理解和设计深度学习模型。本文认为，通过整合拓扑概念，拓扑深度学习可以作为补充图表示学习和几何深度学习的自然选择，并且可以在各种机器学习场景中提供实用的益处和理论基础，并讨论了拓扑深度学习中的一些开放问题，包括潜在解决方案和未来的研究机会。同时，本文也邀请科学界积极参与拓扑深度学习研究，以发掘这个新兴领域的潜力。

拓扑深度学习的挑战与机遇

Position Paper: Challenges and Opportunities in Topological Deep  Learning

Raindrops adhering to the lens of UAVs can obstruct visibility of the
background scene and degrade image quality. Despite recent progress in image
deraining methods and datasets, there is a lack of focus on raindrop removal
from UAV aerial imagery due to the unique challenges posed by varying angles
and rapid movement during drone flight. To fill the gap in this research, we
first construct a new benchmark dataset for removing raindrops from UAV images,
called UAV-Rain1k. In this letter, we provide a dataset generation pipeline,
which includes modeling raindrop shapes using Blender, collecting background
images from various UAV angles, random sampling of rain masks and etc. Based on
the proposed benchmark, we further present a comprehensive evaluation of
existing representative image deraining algorithms, and reveal future research
opportunities worth exploring. The proposed dataset will be publicly available
at this https URL

使用 Blender 模拟雨滴形状，收集不同无人机角度的背景图像，随机采样雨滴遮罩等构建了一个新的用于去除无人机图像中雨滴的基准数据集 UAV-Rain1k，并在此基准数据集上全面评估了现有的代表性图像去雨算法，并公布了值得进一步研究的未来研究机会。