Assessing Gonipterus sp. n. 2 defoliation levels using multispectral Unmanned Aerial Vehicle (UAV) data in Eucalyptus plantations

The locally invasive insect pest Gonipterus sp. n. 2 (Coleoptera: Curculionidae) threatens Eucalyptus plantations, causing defoliation and yield loss through adult and larval feeding. Early detection is important for early intervention to prevent pest outbreaks. As conventional insect pest monitoring methods are time-consuming and spatially restrictive, this study assessed the potential of UAV monitoring. Multispectral imagery was obtained with Unmanned Aerial Vehicles (UAVs) in South Africa’s Midland region across seven different sites in 14 datasets of young stands of Eucalyptus dunnii with varying levels of Gonipterus sp. n. 2 infestation. Reference damage levels were obtained through visual assessments of (n= 80-100) trees at each site. Across sites, a decrease in canopy reflectance in both the visual and the near-infrared domains with increasing damage levels was consistently observed. Several vegetation indices showed consistent patterns, but none showed site independence. XGBoost was used to predict damage levels. The best-performing models included reflectance, vegetation indices and grey-level co-occurrence matrix data. When data from a 10-band multispectral camera were used, the highest classification accuracy was 90% across all sites in classifying defoliation levels. With a classical 5-band multispectral camera, accuracy was 82%, but distinguishing medium damage from absence remained challenging. Regardless the sensor, the method was less reliable when the training and validation sets were completely separated. This study highlights the potential of UAV-based multispectral imagery to assess Gonipterus sp. n. 2 damage, demonstrating reliable upscaling from individual tree assessments to stand scale. However, larger training datasets across multiple damage levels and additional image corrections are required for broader applicability.