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Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Author

Listed:
  • Anton Terentev

    (All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia)

  • Vladimir Badenko

    (Advanced Digital Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Ekaterina Shaydayuk

    (All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia)

  • Dmitriy Emelyanov

    (All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia)

  • Danila Eremenko

    (Advanced Digital Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Dmitriy Klabukov

    (Advanced Digital Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Alexander Fedotov

    (Advanced Digital Technologies, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia)

  • Viktor Dolzhenko

    (All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia)

Abstract

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.

Suggested Citation

  • Anton Terentev & Vladimir Badenko & Ekaterina Shaydayuk & Dmitriy Emelyanov & Danila Eremenko & Dmitriy Klabukov & Alexander Fedotov & Viktor Dolzhenko, 2023. "Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina," Agriculture, MDPI, vol. 13(6), pages 1-16, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1186-:d:1162851
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    References listed on IDEAS

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    1. Elena Gultyaeva & Ekaterina Shaydayuk & Philipp Gannibal, 2021. "Leaf Rust Resistance Genes in Wheat Cultivars Registered in Russia and Their Influence on Adaptation Processes in Pathogen Populations," Agriculture, MDPI, vol. 11(4), pages 1-13, April.
    2. Linsheng Huang & Yong Liu & Wenjiang Huang & Yingying Dong & Huiqin Ma & Kang Wu & Anting Guo, 2022. "Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements," Agriculture, MDPI, vol. 12(1), pages 1-16, January.
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    Cited by:

    1. Li Chen & Yu Wu & Ning Yang & Zongbao Sun, 2025. "Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications," Agriculture, MDPI, vol. 15(16), pages 1-30, August.
    2. Dimitrios Kapetas & Eleni Kalogeropoulou & Panagiotis Christakakis & Christos Klaridopoulos & Eleftheria Maria Pechlivani, 2025. "Comparative Evaluation of AI-Based Multi-Spectral Imaging and PCR-Based Assays for Early Detection of Botrytis cinerea Infection on Pepper Plants," Agriculture, MDPI, vol. 15(2), pages 1-25, January.
    3. Chengkai Yang & Jingkai Lei & Zhihao Liu & Shufeng Xiong & Lei Xi & Jian Wang & Hongbo Qiao & Lei Shi, 2025. "Estimation Model of Corn Leaf Area Index Based on Improved CNN," Agriculture, MDPI, vol. 15(5), pages 1-20, February.

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