COTTON GROWTH MODELING USING UNMANNED AERIAL VEHICLE VEGETATION INDICES

被引：0
|
作者：
Yeom, Junho [1 ]
Jung, Jinha [1 ]
Chang, Anjin [1 ]
Maeda, Murilo [2 ]
Landivar, Juan [2 ]
机构：
[1] Texas A&M Univ, Corpus Christi, TX 78412 USA
[2] Texas A&M AgriLife Res Extens Serv, Corpus Christi, TX USA
来源：
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年
关键词：
Crop growth model; UAV; Vegetation index; PRECISION AGRICULTURE; SYSTEMS; CROP; UAV;
D O I：
暂无
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Unmanned aerial vehicle (UAV) images have great potential for agricultural researches because of their high spatial and temporal resolutions. However, most UAV researches in the agriculture field have adopted vegetation indices without second derivative parameters related with a growth model. In addition, visible band vegetation indices in UAV researches have not been explored in detail despite of their importance in UAV application. In this study, three RGB vegetation indices that showed good performance in previous studies are adopted and growth modeling using time series vegetation indices is proposed. In addition, growth model-based second derivatives are extracted for crop growth analysis. R squares of the proposed method from three RGB vegetation indices were 0.8-0.9 and excessive green vegetation index (ExG) showed the best accuracy.
引用
收藏
页码：5050 / 5052
页数：3
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