您的位置: 首页 > 院士专题 > 专题 > 详情页

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

使用基于无人机的多传感器图像估算棉花产量

关键词:
来源:
Biosystems Engineering
全文链接1:
http://agri.ckcest.cn/topic/downloadFile/37923eea-73c3-44f9-a206-892f8720499d
全文链接2:
https://doi.org/10.1016/j.biosystemseng.2020.02.014
类型:
学术文献
语种:
英语
原文发布日期:
2020-03-08
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 R2 = 0.90. The combination of PH and CFI, PH and a∗, or PH and temperature were the best two-feature models with R2 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.
相关推荐

意 见 箱

匿名:登录

个人用户登录

找回密码

第三方账号登录

忘记密码

个人用户注册

必须为有效邮箱
6~16位数字与字母组合
6~16位数字与字母组合
请输入正确的手机号码

信息补充