Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

Abstract

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.