AI Diagnoses Urban Tree Health from Video in Breakthrough Plant Monitoring System

A game-changing AI system called 'Plant Doctor' can now scan city trees using everyday video cameras, offering accurate health checks without harming plants—revolutionizing how we monitor urban greenery and paving the way for smarter, greener cities.

Research: Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage. Image Credit: Aleksandrs Muiznieks / Shutterstock

Urban trees and plants do more than beautify city landscapes. They purify the air, reduce urban heat islands, provide recreational spaces, and even boost property values. Plants silently contribute to our well-being as essential components of sustainable urban ecosystems. However, urban trees face many threats, including pests, diseases, and climate change, making keeping their health in check essential.

Urban greenery monitoring has traditionally been labor-intensive, requiring botanical expertise and considerable resources. With cities expanding worldwide and urban environments becoming more complex, keeping track of plant health has also become more complicated. Could artificial intelligence (AI) hold the key to addressing this challenge?

In a recent study, a joint research team led by Professor Umezu's Laboratory from the Department of Life Science and Medical Bioscience at Waseda University and Professor Shiojiri's Laboratory from the Faculty of Agriculture at Ryukoku University developed an innovative AI-driven solution for monitoring plant health.

Their paper was published online in the journal Measurement and will be published in Volume 249 on May 31, 2025. This study introduces 'Plant Doctor,' a hybrid AI system that automatically diagnoses urban tree health through video footage captured by ordinary cameras. "Machine vision techniques such as segmentation have great applications in the medical field. We wanted to extrapolate this technology to other areas, such as plant health," says first author Marques, explaining their motivation.

Plant Doctor combines two cutting-edge machine vision algorithms, YOLOv8 and DeepSORT, to identify and track individual leaves across video frames. These algorithms aim to ensure that only the best images for each leaf are selected for further processing. Then, a third algorithm, DeepLabV3Plus, performs detailed image segmentation to quantify leaf damage precisely. The proposed system can automatically detect diseased areas on individual leaves, such as spots caused by bacteria, pests, and fungi.

One of the most attractive aspects of this approach is its scalability and cost efficiency. The system can process video footage collected by cameras mounted on drones and city maintenance vehicles like garbage trucks, turning routine services into opportunities to gather data without investing substantial resources. Moreover, by using images rather than actual branches and leaves, Plant Doctor minimizes stress on city plants. "We have provided a tool for botanical experts to assess plant health in one solution without the gathering samples and damaging plants in the process," remarks Marques. The research team validated the proposed system using footage of urban plants in Tokyo, obtaining favorable results and remarkably accurate leaf health diagnoses across various urban flora.

By combining plant health data with accurate location information, Plant Doctor enables both a micro-level analysis of individual plants and macro-level insights into disease patterns across urban areas. Beyond urban applications, Plant Doctor could also be adapted for agricultural use, helping farmers monitor crop health and identify diseases before they spread.

Overall, the proposed technology represents a significant step toward more sustainable urban and rural plant health monitoring. It allows botanical experts to focus more on strategic interventions rather than routine monitoring. Let us hope these efforts lead to cities and fields with healthier plants!