An aerial point cloud dataset of apple tree detection and segmentation with integrating RGB information and coordinate information

Citation Author(s):
Ruizhe
Yang
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Wentai
Fang
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Xiaoming
Sun
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Xudong
Jing
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Longsheng
Fu
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, 712100, China
Xiaofeng
Wei
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Rui
Li
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
Submitted by:
Longsheng Fu
Last updated:
Fri, 06/21/2024 - 14:05
DOI:
10.21227/z2yt-cr21
Data Format:
License:
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Abstract 

Accurate detection and segmentation of apple trees are crucial in high throughput phenotyping, further guiding apple trees yield or quality management. A LiDAR and a camera were attached to the UAV to acquire RGB information and coordinate information of a whole orchard. The information was integrated by simultaneous localization and mapping network to form a dataset of RGB-colored point clouds. The dataset can be used for methods related to apple detection and segmentation based on point clouds.

Comments

Dear Sir,

I would like to research on Apple tree segmentation for branch detection.

Submitted by MD Samiul Islam on Thu, 07/06/2023 - 04:33

Dear Sir,

I would like to research on Apple tree segmentation for branch detection

Submitted by youele wo on Sat, 12/21/2024 - 03:30