Pheno-Robot：一种用于田间原位表型分析的自动数字化建模系统
Pheno-Robot: An Auto-Digital Modelling System for In-Situ Phenotyping in the Field
AI总结：
本研究提出Pheno-Robot自动数字化建模系统，通过环境理解、运动规划和原位表型分析三个模块实现田间植物数据自主采集与高质量模型重建，提升了农业环境表型分析的效率与鲁棒性。
摘要：
在精准农业中，准确重建用于表型分析的植物模型对于优化可持续农业实践至关重要。传统的基于实验室的表型分析虽有价值，但在理解植物在不受控条件下的生长情况方面存在不足。机器人技术为在真实环境中进行大规模、直接表型分析提供了有前景的途径。本研究探索了新兴机器人和数字技术在植物表型分析中的部署，以提升性能和效率。引入了三个关键功能模块：环境理解、机器人运动规划和原位表型分析，以实现整个流程的自动化。实验结果证明了该系统在农业环境中的有效性。Pheno-robot系统通过在植物周围自主导航来收集高质量数据。此外，原位建模模型利用机器人收集的数据重建了高质量的植物模型。所开发的机器人系统展现出高效率和鲁棒性，证明了其在真实农业环境中推动植物科学发展的潜力。
英文摘要：
Accurate reconstruction of plant models for phenotyping analysis is critical for optimising sustainable agricultural practices in precision agriculture. Traditional laboratory-based phenotyping, while valuable, falls short of understanding how plants grow under uncontrolled conditions. Robotic technologies offer a promising avenue for large-scale, direct phenotyping in real-world environments. This study explores the deployment of emerging robotics and digital technology in plant phenotyping to improve performance and efficiency. Three critical functional modules: environmental understanding, robotic motion planning, and in-situ phenotyping, are introduced to automate the entire process. Experimental results demonstrate the effectiveness of the system in agricultural environments. The pheno-robot system autonomously collects high-quality data by navigating around plants. In addition, the in-situ modelling model reconstructs high-quality plant models from the data collected by the robot. The developed robotic system shows high efficiency and robustness, demonstrating its potential to advance plant science in real-world agricultural environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
