期刊
SENSORS
卷 19, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/s19245558
关键词
agricultural unmanned aerial vehicles; monocular computer vision; tree crown segmentation; circumstance brightness; weed environment orchard
资金
- Project of Guangdong Province Support Plans for Top-Notch Youth Talents, China [2016TQ03N704]
- Planned Science and Technology Project of Guangdong Province, China [2019A050510045, 2019B020216001]
- Planned Science and Technology Project of Guangzhou, China [201704020076, 201904010206]
- Innovative Project for University of Guangdong Province [2017KTSCX099]
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, P.R. China [2018ZJUGP001]
- Guangdong Academy of Sciences Special Fund for the Building of First-Class Research Institutions in China [2019GDASYL-0502007]
- Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (Climbing Program Special Funds) [PDJH2019B0244]
- Science and Technology Innovation Fund for Graduate Students of Zhongkai University of Agricultural and Engineering [KJCX2019007]
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.
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