Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina
| Autor: | Anton Terentev, Vladimir Badenko, Ekaterina Shaydayuk, Dmitriy Emelyanov, Danila Eremenko, Dmitriy Klabukov, Alexander Fedotov, Viktor Dolzhenko |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Agriculture, Vol 13, Iss 6, p 1186 (2023) |
| Druh dokumentu: | article |
| ISSN: | 13061186 2077-0472 |
| DOI: | 10.3390/agriculture13061186 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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