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Map of winter crops of the season 2017 placed on the service VEGA: Results of selective quality controlWinter crop masks, based on MODIS satellite data, are regularly laid out from the Internet service
Reasons for long-term dynamics of NDVI (MODIS) averaged for arable lands of municipalities of Belgorod region significant factors are the changes in crops acreage and the climatic conditions of the growing season
Карта озимых культур сезона 2017 года, размещенная на сервисе ВЕГА: результаты выборочной проверки качестваWinter crop masks, based on MODIS satellite data, are regularly laid out from the Internet service
Convolutional neural network in the recognition of spatial images of sugarcane crops in the troncal region of the coast of Ecuador. The experiment showed affirmative results of approximately 95% probability of recognition of crop status. © 2019
MAPPING CROPLANDS with A LONG HISTORY of CROP CULTIVATION USING TIME SERIES of MODIS VEGETATION INDICES and implemented. The methodology is based on a new recognition feature enabling identification of all lands
Convolutional neural network based pest and disease recognition learning, has good generalization and accuracy. Crop pest and disease identification combined with image
Estimating the soil erosion cover-management factor at the european part of Russia to achieve this goal. The accuracy of crop group recognition compared to the open data of the Federal State
Extraction of Human Body Parts in Image Using Convolutional Neural Network and Attention Model. The algorithm is a part of Smart Cropping system developed by us which aim is to cut the images and prepare e
Некоторые аспекты разработки сервиса по распознаванию болезней сельскохозяйственных культур. Every year millions of tons of crops are lost due to plant diseases, which leads to economic losses
The resource potential of Russian lands for crop farming crops in Russia is about 10% of the available land at best (for summer wheat, buckwheat
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