An Integrated System for Mapping Red Clover Ground Cover Using Unmanned Aerial Vehicles, A Case Study in Precision Agriculture

被引：6

作者：

Abuleil, Ammar M. ^{[1
]}

Taylor, Graham W. ^{[1
]}

Moussa, Medhat ^{[1
]}

机构：

[1] Univ Guelph, Sch Engn, Guelph, ON, Canada

来源：

2015 12TH CONFERENCE ON COMPUTER AND ROBOT VISION CRV 2015 | 2015年

关键词：

precision agriculture; remote sensing; machine learning; red clover; ground cover; classification; IMAGERY; CLASSIFICATION;

D O I：

10.1109/CRV.2015.43

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of precision agriculture (PA), Unmanned Aerial Vehicles (UAVs) are creating new opportunities for remotely assessing various characteristics of crops. In this paper, we present two main contributions that were evaluated on a novel application: mapping red clover ground cover (RCGC). First, we develop an integrated system for collecting, preprocessing and analyzing aerial data for the mapping of RCGC at a patch-level. Second, we collected, ground-truthed, and preprocessed a RCGC dataset that we make public for further analysis. We evaluated several different machine learning classifiers for mapping image patches to discrete clover coverage levels, reaching an accuracy of 91%.

引用

页码：277 / 284

页数：8

共 20 条

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