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作者:

Cheng, Xiangzhe (Cheng, Xiangzhe.) | Huang, Mengning (Huang, Mengning.) | Guo, Anting (Guo, Anting.) | Huang, Wenjiang (Huang, Wenjiang.) | Cai, Zhiying (Cai, Zhiying.) | Dong, Yingying (Dong, Yingying.) | Guo, Jing (Guo, Jing.) | Hao, Zhuoqing (Hao, Zhuoqing.) | Huang, Yanru (Huang, Yanru.) | Ren, Kehui (Ren, Kehui.) | Hu, Bohai (Hu, Bohai.) | Chen, Guiliang (Chen, Guiliang.) | Su, Haipeng (Su, Haipeng.) | Li, Lanlan (Li, Lanlan.) | Liu, Yixian (Liu, Yixian.)

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EI Scopus

摘要:

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. © 2024 by the authors.

关键词:

Principal component analysis Classification (of information) Decision trees Forestry Rubber Random forests Reflection Radiometers Wavelet transforms Vegetation mapping Feature extraction Support vector machines Optical remote sensing Fungi Data mining

作者机构:

  • [ 1 ] [Cheng, Xiangzhe]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 2 ] [Cheng, Xiangzhe]Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya; 572029, China
  • [ 3 ] [Cheng, Xiangzhe]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 4 ] [Huang, Mengning]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Guo, Anting]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 6 ] [Guo, Anting]Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya; 572029, China
  • [ 7 ] [Huang, Wenjiang]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 8 ] [Huang, Wenjiang]Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya; 572029, China
  • [ 9 ] [Huang, Wenjiang]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 10 ] [Cai, Zhiying]Yunnan Key Laboratory of Sustainable Utilization Research on Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 11 ] [Cai, Zhiying]National and Local Joint Engineering Research Center of Breeding and Cultivation Technology of Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 12 ] [Dong, Yingying]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 13 ] [Dong, Yingying]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 14 ] [Guo, Jing]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 15 ] [Guo, Jing]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 16 ] [Hao, Zhuoqing]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 17 ] [Hao, Zhuoqing]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 18 ] [Huang, Yanru]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 19 ] [Huang, Yanru]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 20 ] [Ren, Kehui]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 21 ] [Ren, Kehui]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 22 ] [Hu, Bohai]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100094, China
  • [ 23 ] [Hu, Bohai]University of Chinese Academy of Sciences, Beijing; 100049, China
  • [ 24 ] [Chen, Guiliang]Yunnan Key Laboratory of Sustainable Utilization Research on Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 25 ] [Chen, Guiliang]National and Local Joint Engineering Research Center of Breeding and Cultivation Technology of Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 26 ] [Su, Haipeng]Yunnan Key Laboratory of Sustainable Utilization Research on Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 27 ] [Su, Haipeng]National and Local Joint Engineering Research Center of Breeding and Cultivation Technology of Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 28 ] [Li, Lanlan]Yunnan Key Laboratory of Sustainable Utilization Research on Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 29 ] [Li, Lanlan]National and Local Joint Engineering Research Center of Breeding and Cultivation Technology of Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 30 ] [Liu, Yixian]Yunnan Key Laboratory of Sustainable Utilization Research on Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China
  • [ 31 ] [Liu, Yixian]National and Local Joint Engineering Research Center of Breeding and Cultivation Technology of Rubber Tree, Yunnan Institute of Tropical Crops, Jinhong; 666100, China

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来源 :

Remote Sensing

年份: 2024

期: 9

卷: 16

5 . 0 0 0

JCR@2022

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SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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