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Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

文献类型： 外文期刊

作者： Sun, Qian 1 ; Sun, Lin 2 ; Shu, Meiyan 3 ; Gu, Xiaohe 1 ; Yang, Guijun 1 ; Zhou, Longfei 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Shandong Univ Sci & Technol, Coll Geomat, Qingdao, Peoples R China

3.China Agr Univ, Coll Resources & Environm Sci, Beijing, Peoples R China

期刊名称：PLANT PHENOMICS （ 影响因子：6.961； 五年影响因子：6.961 ）

ISSN： 2643-6515

年卷期： 2019 年 2019 卷

页码：

收录情况： SCI

摘要： Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

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