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

Anton Terentev (), Vladimir Badenko (), Ekaterina Shaydayuk, Dmitriy Emelyanov, Danila Eremenko, Dmitriy Klabukov, Alexander Fedotov and Viktor Dolzhenko
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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

Agriculture, 2023, vol. 13, issue 6, 1-16

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.

Keywords: hyperspectral remote sensing; wheat leaf rust; Puccinia triticina; support vector machines; early plant disease detection; VIS-NIR spectroscopy; leaf pigments; biochemical profile (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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