@misc{oai:niigata-u.repo.nii.ac.jp:00033908,
 author = {Boiarskii, Boris Sergeevich},
 month = {2020-07-02, 2020-07-02},
 note = {The introduction of new technologies in agriculture stems from the need to improve the quality and profitability of agricultural production. Currently, there is an acute question about the introduction of new technologies in agriculture. Moreover, there are problems in obtaining new knowledge of agronomists and farmers that it becomes impossible to promote such technologies in poorly developed regions. This study showed the use of a UAV and a multispectral camera in assessing the health of crops for identifying areas with depressed vegetation. These technologies are bringing a useful step in the development of an agricultural management system in the direction of increasing the efficiency of land use. Modern hardware, such as multispectral cameras, makes the remote analysis more informative and has a significantly expanded range of applications. We aimed to clarify issues in current soybean production and explain ways to enhance the efficiency of soybean cultivation. We analysed soybean production in the Amur Region of the Russian Federation, which is considered the national leader in the agricultural sector. Results indicated that the main problem is low soybean yield because of a lack of financial resources, which hinders the development of cultivation technologies and local farmers’ capabilities to purchase advanced agricultural machinery. The Region has unpredictable weather conditions and systematic soil waterlogging, which causes additional expenses for farmers. We have started to introduce smart agriculture in the Amur Region, Russia, where soybean production is the main direction in the development of the region. This study aims to expand the application of new technologies in the Amur Region and the Far Eastern part of Russia. We cooperate in joint research work in the field of agriculture through the introduction of smart farming. This study was made possible due to the collaboration of researchers from Faculty of Agriculture of Niigata University and scientific institutions of the Russian Far East (All-Russian Scientific Research Institute of Soybean, Federal East State Agrarian University). Niigata University established the Centre for Research on East Asian Rim, which promotes international exchange, international cooperation, and international collaborative research based on the concept of East Asia Rim. Due to the geographical position of Niigata and the Russian Far East, we focus on strategically development of research work aimed at strengthening relations between these sides. As an experiment, these methods were used to show the application of UAV-derived data in crop analysis. The use of the normalised difference vegetation index (NDVI) in agriculture is beginning to develop rapidly, and the need to introduce these technologies into the agriculture sphere is becoming urgent. Analysis of the NDVI and its comparison with yield showed that in the future this index could be used to predict the yield of soybean and build a mathematical model for predicting the yields of particular soybean varieties. These technologies are bringing a useful step in the development of an agricultural management system in the direction of increasing the efficiency of land use. UAVs provided field surveying and high-resolution monitoring capabilities, which allowed us to estimate different data. They produced precise map data for early soil analysis, which is useful in planning seed planting. This study showed that to increase economic efficiency production and processing of soybean, a comprehensive approach is needed. We showed the use of a UAV and a multispectral camera in assessing the health of crops for identifying areas with depressed vegetation. Moreover, we analysed an experimental field and observed low-lying ground areas on the field, inclined to flood and waterlogging., 新大院博(農)甲第200号},
 title = {Evaluation of the UAV-Based Multispectral Imagery and its Application for Crop Monitoring and Yield Prediction in Russia},
 year = {}
}