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A new comprehensive index for monitoring maize lodging severity using UAV-based multi-spectral imagery

文献类型： 外文期刊

作者： Sun, Qian 1 ; Chen, Liping 2 ; Xu, Xiaobin 4 ; Gu, Xiaohe 1 ; Hu, Xueqian 1 ; Yang, Fentuan 5 ; Pan, Yuchun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

2.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China

5.Jilin Acad Agr Sci, Changchun 130033, Peoples R China

关键词： Maize lodging; fuzzy comprehensive evaluation (FCE); comprehensive lodging evaluation index (CLEI); unmanned aerial vehicle (UAV); Multi-spectral imagery

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 202 卷

页码：

收录情况： SCI

摘要： Lodging significantly reduces crop yield and grain quality. Timely and accurately monitoring crop lodging severity to help farmers to adjust the field management and settle reasonable insurance claim. The purpose of this paper is to effectively monitor maize lodging severity using unmanned aerial vehicle (UAV) with a multi -spectral camera. Considering the plant height (PH), lodging angle (LA) and SPAD, each agronomic trait was transformed into a membership matrix by giving each factor a weight. The comprehensive superiority of each evaluation result based on agronomic trait (PH, LA, SPAD) was calculated by using the fuzzy comprehensive evaluation (FCE) method, and the comprehensive lodging evaluation index (CLEI) was constructed. The relationships between the spectral reflectance (SR), texture feature (TF), vegetation index (VI) and PH, LA, SPAD, CLEI were established. The CLEI was calculated and mapped by using the sensitive features derived from UAV-based multi-spectral imagery. According to the distribution of CLEI, the research area was divided into five grades, including non-lodging (NL), light lodging (LL), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL). The results showed that among the CLEI models constructed by single feature, VI per-formed best and the R-2 of cross-validation was 0.66 which was higher than agronomic trait models (the R-2 of PH, LA, and SPAD models were 0.56, 0.58, 0.41, respectively). The R-2 and nRMSE of cross-validation of the CLEI model constructed by the top three sensitive features (CIre, NDRE, Re_Mea) were 0.80 and 23.78%, while the R-2 of the PH, LA, and SPAD models were 0.74, 0.71, 0.46, respectively. The accuracy of the new comprehensive index (CLEI) model was better than that of single agronomic trait (PH, LA and SPAD) model. The CLEI proposed in this paper using UAV-based multi-spectral imagery can effectively monitor the lodging severity of maize.

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