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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (1): 58-67.doi: 10.12133/j.smartag.2020.2.1.201911-SA002

• 专题--农业遥感与表型信息获取分析 • 上一篇    下一篇

基于纹理特征与植被指数融合的水稻含水量无人机遥感监测

万亮1,2, 岑海燕1,2(), 朱姜蓬1,2, 张佳菲1,2, 杜晓月1,2, 何勇1,2   

  1. 1.浙江大学生物系统工程与食品科学学院,浙江 杭州 310058
    2.农业农村部光谱检测重点实验室,浙江 杭州 310058
  • 收稿日期:2019-11-13 修回日期:2020-02-19 出版日期:2020-03-30 发布日期:2020-04-17
  • 基金资助:
    国家重点研发计划课题(2016YDF0200600);江苏省现代农业装备与技术协同创新中心项目(4091600007)
  • 作者简介:万 亮(1994-),男,博士研究生,研究方向:无人机遥感,Email:liangwan@zju.edu.cn
  • 通讯作者: 岑海燕(1982-),女,博士,研究员,研究方向:农作物光学成像与智能传感技术及装备、高通量植物表型技术、无人机低空遥感,电话:0571-88982527, E-mail:hycen@zju.edu.cn

Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor

Wan Liang1,2, Cen Haiyan1,2(), Zhu Jiangpeng1,2, Zhang Jiafei1,2, Du Xiaoyue1,2, He Yong1,2   

  1. 1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
    2.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
  • Received:2019-11-13 Revised:2020-02-19 Online:2020-03-30 Published:2020-04-17
  • corresponding author: Haiyan Cen E-mail:hycen@zju.edu.cn

摘要:

含水量是表征水稻生理和健康状况的关键参数,精确预测水稻含水量对于水稻育种和大田精准管理具有重要意义。目前,利用无人机搭载光谱图像传感器监测作物生长的研究主要集中在利用植被指数评估作物在单一或者几个生育期的生长参数,针对作物含水量监测的研究非常有限。本研究主要利用多旋翼无人机低空遥感平台获取不同生育期水稻冠层的RGB图像和多光谱图像,通过提取植被指数和纹理特征,分析水稻的动态生长变化,并构建了基于随机森林回归方法的含水量预测模型。试验结果表明:(1)从无人机图像提取的植被指数、纹理特征以及地面测量的含水量都能用于监测水稻生长,并且这些参数随水稻生长呈现出了相似的动态变化趋势;(2)与RGB图像相比,多光谱图像评估水稻含水量具有更高的潜力,其中归一化光谱指数NDSI771,611实现了更好的预测精度(R2=0.68,RMSEP=0.039,rRMSE =5.24%);(3)融合植被指数和纹理特征能够进一步改善含水量的预测结果(R2=0.86,RMSEP=0.026,rRMSE=3.51%),预测误差RMSEP分别减小了16.13%和18.75%。上述结果表明,基于无人机遥感技术监测水稻含水量是可行的,可为农田精准灌溉和田间管理决策提供新思路。

关键词: 无人机低空遥感, 水稻含水量, RGB图像, 多光谱图像, 植被指数, 纹理特征, 特征融合

Abstract:

Water concentration is a key parameter to characterize crop physiological and healthy status. It is of great significance of employing unmanned aerial vehicle (UAV) low-altitude remote sensing technology to predict crop water concentration for crop breeding and precision agriculture management. UAV remote sensing has been widely used for monitoring crop growth status, mainly focusing on using vegetation indices to estimate crop growth parameters at single or several growth stages. Few studies have been performed on evaluating crop water concentration. Consequently, this study mainly used vegetation indices and texture features extracted from UAV-based RGB and multispectral images to monitor water concentration of rice crop during the whole growth period. Firstly, a multi-rotor UAV equipped with high-resolution RGB and multispectral cameras to collect canopy images of rice crop, and water concentration was also measured by ground sampling. Then, vegetation indices and texture features calculated from RGB and multispectral images were used to analyze the growth changes of rice. Finally, random forest regression method was used to establish a prediction model of water concentration based on different image features. The results show that: (1) vegetation index, texture features and ground-measured water concentration could be used to dynamically monitor rice growth, and there existed correlations among these parameters; (2) image features extracted from multispectral images possessed more potential than those from RGB images to evaluate water concentration of rice crop, and normalized difference spectral index NDSI771, 611 achieved the best prediction accuracy (R2 = 0.68, RMSEP = 0.039, rRMSE = 5.24%); (3) fusing vegetation indices and texture features could further improve the prediction of water concentration (R2 = 0.86, RMSEP = 0.026, rRMSE = 3.21%), and the prediction error of RMSEP was reduced by 16.13% and 18.75%, respectively. These results demonstrats that it is feasible to apply UAV-based remote sensing to monitor water concentration of rice crop, which provides a new insight for precision irrigation and decision making of field management.

Key words: unmanned aerial vehicle (UAV), water concentration of rice, RGB image, multispectral image, vegetation indices, texture feature, feature fusion

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