Abstract
The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 274 hectares. The survey was performed by a quadcopter with a MicaSense RedEdge-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. 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.
Highlights
Krasnodar region is the main supplier of rice in Russia
With the development of modern remote sensing technologies, including the use of unmanned aerial vehicles (UAVs), the possibilities of assessing the condition of agricultural crops over large areas have significantly increased while reducing the cost of monitoring rice crops [1, 2]
The GPS receiver mounted on the UAV, when receiving geographical coordinates during the flight, provides an accuracy of 5-10 m
Summary
Krasnodar region is the main supplier of rice in Russia. In 2019, the volume of rice harvested in the Kuban amounted to more than 950 thousand tons, i.e. about 80% of the rice yield in our country. With the development of modern remote sensing technologies, including the use of unmanned aerial vehicles (UAVs), the possibilities of assessing the condition of agricultural crops over large areas have significantly increased while reducing the cost of monitoring rice crops [1, 2]. The implementation of such technologies, coupled with appropriate techniques and the accumulation of arrays of experimental observations, can improve the quality of forecasting the yield of rice crops [6] and reduce the cost of land reclamation measures. A number of publications by Chinese researchers [8, 15] have implemented models for calculating rice yields based on the cumulative NDVI index based on data from UAV surveys for the entire growing season of the crop
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