中国农业科学院农业信息研究所 中国农学会
Agricultural Information Institute of CAAS China Association of Agricultural Science Societies
En
Ch
中国农业科学院农业信息研究所 中国农学会 主办

AI检索

订阅

返回顶部
当前位置: 首页 > 文章 > New method for cotton fractional vegetation cover extraction based on UAV RGB images 国际农业与生物工程学报 2022,15 (4)
Position: Home > Articles > New method for cotton fractional vegetation cover extraction based on UAV RGB images International Journal of Agricultural and Biological Engineering 2022,15 (4)

New method for cotton fractional vegetation cover extraction based on UAV RGB images

作  者：
Huanbo Yang;Yubin Lan;Liqun Lu;Daocai Gong;Jianchi Miao;Jing Zha
单  位：
2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, Chin; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China;1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 3. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, Shandong, China;2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China
关键词：
cotton;UAV;visible light images;fractional vegetation cover;vegetation index threshold method;TRVI;TBV
摘  要：
As the key principle of precision farming, the distribution of fractional vegetation cover is the basis of crop management within the field serves. To estimate crop FVC rapidly at the farm scale, high temporal-spatial resolution imagery obtained by unmanned aerial vehicle (UAV) was adopted. To verify the application potential of consumer-grade UAV RGB imagery in estimated FVC, blue-green characteristic vegetation index (TBVI) and red-green vegetation index (TRVI) were proposed in this study according to the differences of the gray value among cotton vegetation, soil and shadow in the field. First, two new constructed indices and several published indices were used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the thresholds of cotton vegetation and non-vegetation pixels were established based on the vegetation index threshold method which combines support vector machine classification and vegetation index. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices was compared. The results show that the accuracy of the information extracted by TRVI is higher than that of subdivision index of other visible light (FVC extraction precision in the first bud stage of cotton: R-2=0.832, RMSE=2.307, nRMSE=4.405%; FVC extraction precision in the bud stage of cotton: R-2=0.981, RMSE=1.393, nRMSE=1.984%; FVC extraction precision in the flowering stage of cotton: R-2=0.893, RMSE=2.101, nRMSE=2.422%; FVC extraction precision in the boll stage of cotton: RV=0.958, RMSE=1.850, nRMSE=2.050%).

相似文章

导出引用
计量
文章访问数: 5
HTML全文浏览量: 0
PDF下载量: 0

所属期刊

推荐期刊

友情链接Friendly link
农业信息研究所Agricultural information institute 农业智能知识服务平台Agricultural intelligent knowledge service platform 农业学术服务平台Agricultural Knowledge Service System 中国知网CNKI 维普网VIP 万方WANFANG DATA
联系我们Contact us

电话Tell：010-82109652-808

邮箱E-mail：agrijournal@caas.cn

服务Service
关于我们About us 意见建议Opinions and suggest
版权说明Copyright
单位：中国农业科学院农业信息研究所 中国农学会 © 2017中国农业科学院农业信息研究所 ICP备案号：京ICP备09089781号-46