UAV-based spectral images using remote sensing and YOLOv8 in <i>Eucalyptus saligna</i> Sm. inventory

Accurate and low-cost tree inventories in forest plantations are essential for an effective production management. Stimulated by recent advancements in Unmanned Aerial Vehicle (UAV) imagery coupled with artificial intelligence, and by the interest in developing models capable of supporting decision-making on silvicultural and forest management, this study aimed to evaluate the performance of different vegetation indices in detecting Eucaliptus saligna individuals by using an improved deep learning model. The tree-individual detection model was created using the YOLOv8n algorithm using UAV RGB images and vegetation indices (VI) generated from the multispectral sensor onboard the UAV. Nine VIs were selected for training (65%) and testing (35%) the models. The proposed framework demonstrated that the MPRI, PSRI, and NDVI indices achieved an F1 score of 0.98 and a precision of 0.97 for detecting E. saligna individual trees six months after planting. Our study demonstrates the robustness of the proposed framework and recommends the application of the MPRI index in individual tree detection due to its efficient performance, cost-effectiveness, and simplicity, as it only utilizes regions of the visible spectrum.