植物医生：一种混合机器学习与图像分割软件，用于量化视频影像中的植物损伤
Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage
AI总结：
本文提出Plant Doctor系统，结合YOLOv8、DeepSORT和DeepLabV3Plus，从视频中自动跟踪叶片并分割量化病害损伤，实现城市植物健康的高效监测。
摘要：
人工智能显著推进了诊断过程的自动化，使包括农业在内的多个领域受益。本研究介绍了一种基于人工智能的系统，利用可获取的摄像设备拍摄的视频影像对城市街道植物进行自动诊断。该系统旨在日常监测植物健康状况，协助控制城市地区病害的传播。通过结合两种机器视觉算法——YOLOv8和DeepSORT，该系统能够高效识别并跟踪单片叶子，提取用于健康分析的最佳图像。YOLOv8因其速度和计算效率而被选用，用于定位叶片，而DeepSORT确保在复杂环境中进行稳健跟踪。对于详细的健康评估，采用卷积神经网络DeepLabV3Plus来分割并量化由细菌、害虫和真菌引起的叶片损伤。该混合系统名为Plant Doctor，已使用包含东京城市植物影像的多样化数据集进行训练和验证。结果表明，该系统在诊断叶片损伤方面具有稳健性和准确性，并具有在大规模城市植物病害监测中的应用潜力。该方法为城市树木健康管理提供了一种非侵入性、高效且可扩展的解决方案，支持可持续的城市生态系统。
英文摘要：
Artificial intelligence has significantly advanced the automation of diagnostic processes, benefiting various fields including agriculture. This study introduces an AI-based system for the automatic diagnosis of urban street plants using video footage obtained with accessible camera devices. The system aims to monitor plant health on a day-to-day basis, aiding in the control of disease spreading in urban areas. By combining two machine vision algorithms, YOLOv8 and DeepSORT, the system efficiently identifies and tracks individual leaves, extracting the optimal images for health analysis. YOLOv8, chosen for its speed and computational efficiency, locates leaves, while DeepSORT ensures robust tracking in complex environments. For detailed health assessment, DeepLabV3Plus, a convolutional neural network, is employed to segment and quantify leaf damage caused by bacteria, pests, and fungi. The hybrid system, named Plant Doctor, has been trained and validated using a diverse dataset including footage from Tokyo urban plants. The results demonstrate the robustness and accuracy of the system in diagnosing leaf damage, with potential applications in large scale urban flora illness monitoring. This approach provides a non-invasive, efficient, and scalable solution for urban tree health management, supporting sustainable urban ecosystems.
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。
