Paper Info
Title
Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment.
Abstract
The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% +/- 9.43%; thus, the proposed method achieved better performance than two similar methods.
Year
DOI
Venue
2019
10.3390/s19245558
SENSORS
Keywords
DocType
Volume
agricultural unmanned aerial vehicles,monocular computer vision,tree crown segmentation,circumstance brightness,weed environment orchard
Journal
19
Issue
ISSN
Citations 
24.0
1424-8220
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Yayong Chen100.34
Chaojun Hou201.01
Yu Tang351.11
Jiajun Zhuang401.01
Jintian Lin500.34
Yong He64415.57
Qiwei Guo700.34
Zhenyu Zhong800.34
Huan Lei900.34
Shaoming Luo1000.34