Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field

被引：4

作者：

Ram, Billy Graham ^{[1
]}

Zhang, Yu ^{[1
]}

Costa, Cristiano ^{[1
]}

Ahmed, Mohammed Raju ^{[1
]}

Peters, Thomas ^{[2
]}

Jhala, Amit ^{[3
]}

Howatt, Kirk ^{[2
]}

Sun, Xin ^{[1
]}

机构：

[1] North Dakota State Univ, Dept Agr & Biosyst Engn, Fargo, ND 58108 USA

[2] North Dakota State Univ, Dept Plant Sci, POB 6050, Fargo, ND 58108 USA

[3] Univ Nebraska Lincoln, Dept Agron & Hort, Lincoln, NE 68583 USA

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2023年 / 215卷

基金：

美国食品与农业研究所;

关键词：

Palmer amaranth; Hyperspectral imaging; Machine learning; Multivariate data analysis; RANDOM FOREST; CLASSIFICATION; INTERFERENCE; SPECTROSCOPY;

D O I：

10.1016/j.compag.2023.108444

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

This material is based on work partially supported by the U.S. Department of Agriculture, agreement number 58-6064-8-023. This work is/was supported by the USDA National Institute of Food and Agriculture, Hatch project number ND01487. The authors want to thank all the collaborators at the University of Nebraska-Lincoln that contributed the field for data collection in this research. The authors also want to thank the NDSU Plant Science and Agricultural and Biosystems Engineering department research specialists for their help in this study's field data collection coordination and on-site help for graduate student assistant guidance. Lastly, we would like to express our gratitude to Dr. Igathinathane Cannayen, faculty member of the Department of

引用

页数：11

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