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A UAV-based framework for crop lodging assessment

文献类型： 外文期刊

作者： Li, Xiaohan 1 ; Li, Xuezhang 2 ; Liu, Wen 1 ; Wei, Benhui 4 ; Xu, Xianli 2 ;

作者机构： 1.Hunan Normal Univ, Coll Resources & Environm Sci, Changsha 410125, Peoples R China

2.Chinese Acad Sci, Inst Subtrop Agr, Key Lab Agroecol Proc Subtrop Reg, Huanjiang Observat & Res Stn Karst Ecosyst, Changsha 410125, Peoples R China

3.Hunan Normal Univ, Hunan Key Lab Geospatial Big Data Min & Applicat, Changsha 410125, Peoples R China

4.Guangxi Acad Agr Sci, Cash Crops Res Inst, Nanning 530007, Peoples R China

关键词： Crop lodging assessment; UAV visible imagery; Feature selection; Boruta algorithm

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.124； 五年影响因子：5.567 ）

ISSN： 1161-0301

年卷期： 2021 年 123 卷

页码：

收录情况： SCI

摘要： Crop lodging assessment needs to be carried out timely and accurately to ensure valuable information about the location and area where lodging occurs. Many applications have been explored and tested for unmanned aerial vehicle (UAV) visible imagery in agricultural management due to the ability of providing high-space-resolution information. However, there still face many challenges in extracting lodging information using UAV visible imagery, and lacks consensus on an appropriate way to assess crop lodging. The main purpose of this study was to proposed an efficient framework to identify crop lodging at the field scale using UAV visible imagery. This framework contained a two-phase procedure. Meanwhile, three methods were evaluated by providing the appropriate feature subset for objected-based classification to determine the best feature selection method. The results showed that the proposed framework provided high accuracy (94.0 %) for identification of sugarcane lodging. Furthermore, the Boruta algorithm yielded the best feature subset compared with statistical indicators and the RFE algorithm. Thus, the proposed framework based on UAV visible imagery is promising to identify crop lodging precisely, and has great application potential in precision agriculture.

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