Anathi, a researcher in the Department of Forest and Wood Science, is using drone imagery and machine learning to exploring innovative ways to monitor and manage young plantation systems. Her project analyses orthomosaic data to assess how competing vegetation varies across different planting spacings, tree heights, and terrain conditions, incorporating environmental factors such as the Topographic Wetness Index to better understand hydrological influences on growth.

A central part of her work involves generating high-quality training data for artificial intelligence models. Drone imagery is divided into smaller sections and annotated using a combination of manual refinement and AI-assisted tools, including the Segment Anything Model (SAM) within CVAT. These annotations are then used to train, validate, and test segmentation models capable of automatically distinguishing between planted trees and competing vegetation across the IMPACT Open-Air Lab research plantation.
In addition, her research compares RGB and multispectral imagery to evaluate their effectiveness in vegetation detection. This comparison aims to determine whether multispectral data provides a measurable improvement over standard RGB imagery, contributing to more accurate and efficient plantation monitoring approaches.


