Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

Yayong Chen; Chaojun Hou; Yu Tang; Jiajun Zhuang; Jintian Lin; Yong He; Qiwei Guo; Zhenyu Zhong; Huan Lei; Shaoming Luo

doi:10.3390/s19245558

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

Sensors (Basel). 2019 Dec 16;19(24):5558. doi: 10.3390/s19245558.

Authors

Yayong Chen¹, Chaojun Hou¹, Yu Tang¹, Jiajun Zhuang¹, Jintian Lin¹, Yong He², Qiwei Guo¹, Zhenyu Zhong³, Huan Lei³, Shaoming Luo¹

Affiliations

¹ Academy of Contemporary Agriculture Engineering Innovations, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
² College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
³ Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou 510070, China.

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.

Keywords: agricultural unmanned aerial vehicles; circumstance brightness; monocular computer vision; tree crown segmentation; weed environment orchard.