Data-Efficient AI for Precision Weed Monitoring in Tropical Forests
Role
Principal Investigator
Description
Reforestation plays a critical role in enhancing climate resilience, increasing carbon sequestration, and supporting ecosystem restoration in tropical regions. However, early-stage seedlings face a major threat from the rapid spread of aggressive weed species that compete for essential resources such as light, nutrients, and water. Traditional weed control approaches are labour-intensive, costly, and unsuitable for large or remote reforestation sites, making automated solutions highly desirable. This PhD project aims to develop AI-driven computer vision systems for precise and efficient weed detection in tropical reforestation settings. Using aerial and ground imagery, the project will focus on building robust and accurate algorithms capable of operating under challenging environmental conditions. To reduce reliance on large annotated datasets, the research will explore data-efficient learning strategies, enabling scalable and cost-effective model development. The system will be designed for deployment on drones and ground-based platforms, supporting real-time monitoring and decision-making in the field. By improving the accuracy and efficiency of weed detection, the project will help land managers target interventions more effectively, enhance seedling survival rates, and increase the overall success of reforestation programs. This work will contribute to advancing the application of AI and computer vision for environmental restoration, with strong pathways for real-world adoption and commercialisation through industry collaboration.
Date
01 Mar 2026 - 31 Dec 2029
Project Type
N/A
Keywords
Computer vision;deep learning;weed detection;forest
Funding Body
Native Conifer Carbon Sink Pty Ltd
Amount
60668
Project Team
Tao Huang