Estimating plant distance in maize using Unmanned Aerial Vehicle (UAV)

被引:14
作者
Zhang, Jinshui [1 ,2 ]
Basso, Bruno [2 ,3 ,4 ,5 ]
Price, Richard F. [2 ]
Putman, Gregory [2 ]
Shuai, Guanyuan [2 ]
机构
[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
[2] Michigan State Univ, Dept Earth & Environm Sci, E Lansing, MI 48824 USA
[3] Michigan State Univ, WK Kellogg Biol Stn, E Lansing, MI 48824 USA
[4] Queensland Univ Technol, Inst Future Environm & Sci, Brisbane, Qld, Australia
[5] Queensland Univ Technol, Engn Fac, Brisbane, Qld, Australia
来源
PLOS ONE | 2018年 / 13卷 / 04期
关键词
VEGETATION INDEXES; HOUGH TRANSFORM; AUTOMATED CROP; GRAIN-YIELD; VISION; CORN; VARIABILITY; ROWS;
D O I
10.1371/journal.pone.0195223
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Distance between rows and plants are essential parameters that affect the final grain yield in row crops. This paper presents the results of research intended to develop a novel method to quantify the distance between maize plants at field scale using an Unmanned Aerial Vehicle (UAV). Using this method, we can recognize maize plants as objects and calculate the distance between plants. We initially developed our method by training an algorithm in an indoor facility with plastic corn plants. Then, the method was scaled up and tested in a farmer's field with maize plant spacing that exhibited natural variation. The results of this study demonstrate that it is possible to precisely quantify the distance between maize plants. We found that accuracy of the measurement of the distance between maize plants depended on the height above ground level at which UAV imagery was taken. This study provides an innovative approach to quantify plant-to-plant variability and, thereby final crop yield estimates.
引用
收藏
页数:22
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