  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.
