A UAV-based framework for crop lodging assessment
A UAV-based framework for crop lodging assessment
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DOI：
10.1016/j.eja.2020.126201
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发表时间：
2021-02-01
影响因子：
5.2
通讯作者：
Xu, Xianli
中科院分区：
文献类型：
--
作者：
Li, Xiaohan;Li, Xuezhang;Xu, Xianli
需要及时和准确地进行作物倒伏评估，以确保关于发生倒伏的地点和区域的有价值的信息。由于无人机能够提供高空间分辨率的信息，无人机可见光图像在农业管理中的许多应用都得到了探索和测试。然而，在利用无人机可见光图像提取倒伏信息方面仍然面临着许多挑战，并且在评估作物倒伏的适当方法上缺乏共识。这项研究的主要目的是提出一种利用无人机可见光图像在田间尺度上识别作物倒伏的有效框架。该框架包含一个分两个阶段的程序。同时，通过为基于对象的分类提供合适的特征子集，对三种方法进行了评估，以确定最佳的特征选择方法。结果表明，该框架对甘蔗倒伏的识别准确率较高(94.0%)。此外，与统计指标和RFE算法相比，Boruta算法得到了最优的特征子集。因此，基于无人机可见光图像的作物倒伏识别框架具有很好的应用前景，在精准农业中具有很大的应用潜力。
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.