Sereda Igor, Danilov Roman, Kremneva Oksana, Zimin Mikhail, Podushin Yuri
Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia.
Federal State Budgetary Scientific Institution "Federal Research Center of Biological Plant Protection" (FSBSI FRCBPP), 350039 Krasnodar, Russia.
Plants (Basel). 2023 Sep 10;12(18):3223. doi: 10.3390/plants12183223.
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.
利用地球遥感光谱设备和原始监测工具开发远程诊断作物状况的方法,是解决小麦农田生态系统疾病监测问题最具前景的方案。在生物植物保护联邦研究中心的试验田上设立了一个研究点。在总面积为1公顷的试验田内,划分出试验小区以营造人工感染背景,并对相应的对照小区使用杀菌剂进行处理。研究方法基于高精度地面光谱测量与卫星和无人机遥感测量的时间同步,以及将所得数据与植物病理学实地调查数据进行比较。我们的结果表明,受影响最小的植株在整个生长季节中,在绿色、红色和红边光谱范围内的反射率值主要较低,而在近红外范围内的值较高。使用卫星图像和无人机搭载的多光谱相机时,最具信息价值的光谱范围是红色和红外范围。同时,高测量频率对于确定致病背景水平至关重要。我们得出结论,在进行地面光谱测量时,信息采集密度的作用不如测量的重复性显著。利用植被指数评估各种测量系统光谱图像的动态变化,能使它们达到相似的值。