您好，欢迎访问江苏省农业科学院 机构知识库！
江苏省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Yield estimation in cotton using UAV-based multi-sensor imagery

文献类型： 外文期刊

作者： Feng, Aijing 1 ; Zhou, Jianfeng 1 ; Vories, Earl D. 2 ; Sudduth, Kenneth A. 3 ; Zhang, Meina 1 ;

作者机构： 1.Univ Missouri, Div Food Syst & Bioengn, Columbia, MO 65211 USA

2.USDA ARS, Cropping Syst & Water Qual Res Unit, Portageville, MO 63873 USA

3.USDA ARS, Cropping Syst & Water Qual Res Unit, Columbia, MO 65211 USA

4.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Jiangsu, Peoples R China

关键词： Yield estimation; cotton; remote sensing; unmanned aerial vehicle (UAV); imagery

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

ISSN： 1537-5110

年卷期： 2020 年 193 卷

页码：

收录情况： SCI

摘要： Monitoring crop development and accurately estimating crop yield are important to improve field management and crop production. This study aimed to evaluate the performance of an unmanned aerial vehicle (UAV)-based remote sensing system in cotton yield estimation. A UAV system, equipped with an RGB camera, a multispectral camera, and an infrared thermal camera, was used to acquire images of a cotton field at two growth stages (flowering growth stage and shortly before harvest). Sequential images from the three cameras were processed to generate orthomosaic images and a digital surface model (DSM), which were registered to the georeferenced yield data acquired by a yield monitor mounted on a harvester. Eight image features were extracted, including normalised difference vegetation index (NDVI), green normalised difference vegetation index (GNDVI), triangular greenness index (TGI), a channel in CIE-LAB colour space (a*), canopy cover, plant height (PH), canopy temperature, and cotton fibre index (CFI). Models were developed to evaluate the accuracy of each image feature for yield estimation. Results show that PH and CFI were the best single features for cotton yield estimation, both with R-2 = 0.90. The combination of PH and CFI, PH and a*, or PH and temperature were the best two-feature models with R-2 from 0.92 to 0.94. The best three-feature models were among the combinations of PH, CFI, temperature and a*. This study found that UAV-based images collected during the flowering growth stage and/or shortly before harvest were able to estimate cotton yield accurately. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：江苏省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台