Automated Detection of Pineapple Plants in Drone-Captured Aerial Imagery for Precision Agriculture - Lima, Matheus Arroyo de; Martins, Thiago Mantovani; Almeida, Vitor Matheus Soares Siqueira De; Jardim, Rafael Buosi; Zani, Victor Hugo; Barbosa, Luiza Hoehl Loureiro Alves (Anais do Workshop de Visão Computacional (WVC); 2024: Anais do XIX Workshop de Visão ComputacionalDO - 10.5753/wvc.2024.34011, 2024)
Pineapple harvesting remains largely manual due to scattered planting patterns and complex fruit structure. This study presents a method for detecting pineapple plants in large orthomosaic images using the Slicing Aided Hyper Inference (SAHI) technique combined with the YOLOv8 segmentation model. SAHI divides large images into smaller patches, enabling accurate detection. A dataset of 867 training and 97 validation images from two orthomosaics was used, with the model achieving 93% precision and 88% accuracy. Despite high precision, chal lenges with false negatives suggest future improvements. This approach shows promise for automating pineapple harvesting and improving agricultural efficiency.