外文摘要：Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.
外文关键词：remote sensing;machine learning;Smart farming;tree detection;Detectron2;YOLOv8
作者：Moysiadis, Vasileios;Siniosoglou, Ilias;Kokkonis, Georgios;Argyriou, Vasileios;Lagkas, Thomas;Goudos, Sotirios K;Sarigiannidis, Panagiotis
作者单位：Aristotle Univ Thessaloniki;Univ Western Macedonia;MetaMind Innovat PC;Int Hellen Univ;Kingston Univ
期刊名称：AGRICULTURE-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14(2)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台