A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs

Abstract

Citrus (Citrus reticulata), which is an important economic crop worldwide, is often managed in a labor-intensive and inefficient manner in developing countries, thereby necessitating more rapid and accurate alternatives to field surveys for improved crop management. In this study, we propose a novel method for individual tree segmentation from unmanned aerial vehicle remote sensing (RS) using a combination of geographic object-based image analysis (GEOBIA) and layer-adaptive Euclidean distance transformation-based watershed segmentation (LAEDT-WS). First, we use a GEOBIA support vector machine classifier that is optimized for features and parameters to identify the boundaries of citrus tree canopies accurately by generating mask images. Thereafter, our LAEDT workflow separates connected canopies and facilitates the accurate segmentation of individual canopies using WS. Our method exhibited an F1-score improvement of 10.75% compared to the traditional WS method based on the canopy height model. Furthermore, it achieved 0.01% and 1.38% higher F1-scores than the state-of-the-art deep learning detection networks YOLOX and YOLACT, respectively, on the test plot. Our method can be extended to detect larger-scale or more complex structured crops or economic plants by introducing more finely detailed and transferable RS images, such as high-resolution or LiDAR-derived images, to improve the mask base map.