Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses

Author:

Sereda Igor1,Danilov Roman2ORCID,Kremneva Oksana2ORCID,Zimin Mikhail1,Podushin Yuri3

Affiliation:

1. Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia

2. Federal State Budgetary Scientific Institution “Federal Research Center of Biological Plant Protection” (FSBSI FRCBPP), 350039 Krasnodar, Russia

3. Federal State Budgetary Educational Institution “Kuban State Agrarian University Named after I.T. Trubilin”, 350004 Krasnodar, Russia

Abstract

The development of remote methods for diagnosing the state of crops using spectral equipment for remote sensing of the Earth and original monitoring tools is the most promising solution to the problem of monitoring diseases of wheat agrocenoses. A research site was created on the experimental field of the Federal Research Center of Biological Plant Protection. Within the experimental field with a total area of 1 ha, test plots were allocated to create an artificial infectious background, and the corresponding control plots were treated with fungicides. The research methodology is based on the time synchronization of high-precision ground-based spectrometric measurements with satellite and unmanned remote surveys and the comparison of the obtained data with phytopathological field surveys. Our results show that the least-affected plants predominantly had lower reflectance values in the green, red, and red-edge spectral ranges and high values in the near-infrared range throughout the growing season. The most informative spectral ranges when using satellite images and multispectral cameras placed on UAVs are the red and IR ranges. At the same time, the high frequency of measurements is of key importance for determining the level of pathogenic background. We conclude that information acquisition density does not play as significant of a role as the repetition of measurements when carrying out ground-based spectrometry. The use of vegetation indices in assessing the dynamics of the spectral images of various survey systems allows us to bring them to similar values.

Funder

Russian Science Foundation

Publisher

MDPI AG

Subject

Plant Science,Ecology,Ecology, Evolution, Behavior and Systematics

Reference52 articles.

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Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. IMPROVED YOLOv8-BASED AUTOMATED DETECTION OF WHEAT LEAF DISEASES;INMATEH Agricultural Engineering;2023-12-31

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