文章基本信息

标题：Rice crops research according to remote sensing data (overview)
本地全文：下载
作者：Michael Skazhennik ; Vitaly Chizhikov ; Anna Shevchenko 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：285
页码：1-11
DOI：10.1051/e3sconf/202128502038
语种：English
出版社：EDP Sciences
摘要：The introduction of precision farming technologies using hightech equipment will increase the productivity of rice, reduce its cost, and improve the environment. The use of digital technologies in agriculture is especially relevant in the face of rising prices for seeds, fertilizers and fuel, as it helps to significantly reduce costs and increase the profitability of agribusiness. The paper reviews the use of unmanned aerial vehicles (UAV) in rice cultivation and describes methods for assessing the state of rice crops. Drones are a more versatile and efficient tool for obtaining data on the state of crops of crops compared to information obtained from satellite images. They allow real-time monitoring of the most important indicators of the state of crops, which allows agricultural producers to make timely decisions. The UAV was used to determine the boundaries of the rice system, terrain, microreliefs of checks, moisture of the surface soil layer and the state of rice crops. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 327 hectares. The main cultivated crop is rice variety Flagman. The survey was performed by a quadcopter with a Mica Sense Red Edge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite data covering the entire analyzed period (06.05.2019 – 08.29.2019). To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.