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Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery

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Listed:
  • Chunfeng Gao

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Xingjie Ji

    (Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China
    Henan Institute of Meteorological Sciences, Zhengzhou 450003, China)

  • Qiang He

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Zheng Gong

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Heguang Sun

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Tiantian Wen

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Wei Guo

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

Abstract

Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB.

Suggested Citation

  • Chunfeng Gao & Xingjie Ji & Qiang He & Zheng Gong & Heguang Sun & Tiantian Wen & Wei Guo, 2023. "Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery," Agriculture, MDPI, vol. 13(2), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:293-:d:1046848
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    References listed on IDEAS

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    Cited by:

    1. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.

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