.png){kind=icon}
返回
Graph-based methods for analyzing orchard tree structure using noisy point cloud data
.png){kind=icon}
OA
AI
DOI：10.1016/j.compag.2021.106270.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Digitisation of fruit trees using LiDAR enables analysis which can be used to better growing practices to improve yield. Sophisticated analysis requires geometric and semantic understanding of the data, including the ability to discern individual trees as well as identifying leafy and structural matter. Extraction of this information should be rapid, as should data capture, so that entire orchards can be processed, but existing methods for classification and segmentation rely on high-quality data or additional data sources like cameras. We present a method for analysis of LiDAR data specifically for individual tree location, segmentation and matter classification, which can operate on low-quality data captured by handheld or mobile LiDAR. Our methods for tree location and segmentation improved on existing methods with an F1 score of 0.774 and a v-measure of 0.915 respectively, while trunk matter classification performed poorly in absolute terms with an average F1 score of 0.490 on real data, though consistently outperformed existing methods and displayed a significantly shorter runtime.
Keyword:
Agriculture
Lidar
Ceptometer
Light interception
Orchard
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Does it take older adults longer than younger adults to perceptually segregate a speech target from a background masker?在感知上将语音目标与背景掩蔽器隔离开来是否需要老年人比年轻人更长的时间？
PREAI
分享收藏分享收藏
查看更多内容