Green urban garden landscape simulation platform based on high-resolution image recognition technology and GIS

Green urban garden landscape architects, primarily for large-scale applications, such as planning and managing the local environment, ecology and natural resources, have been using a Geographic Information System (GIS). GIS applications explore an urban landscape of the block with the local communit...

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Bibliographic Details
Published in:Microprocessors and microsystems 2021-04, Vol.82, p.1
Main Authors: Zhou, Hui, Dai, Zhili
Format: Article
Language:English
Subjects:
Algorithms
Cereals
Cotton
Crop production
Data sources
Digital mapping
Extraction procedures
Geographic information systems
Image resolution
Landscape architecture
Natural resources
Object recognition
Oilseed crops
Urban environments
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Summary:Green urban garden landscape architects, primarily for large-scale applications, such as planning and managing the local environment, ecology and natural resources, have been using a Geographic Information System (GIS). GIS applications explore an urban landscape of the block with the local community analysis is very difficult. Geographic Information System (GIS) provides a means to collect and use geographic data to support the development of agricultural technology. The proposed Support Geographic Regression (SGR) algorithm is responsible for assessing the conditions and global growth estimate crop area, yield and production of crops, cereals, oilseeds and cotton. Digital map is usually higher than printed on paper. The digital version can be used for the analysis presented in combination with other information data sources in much the same map graphics. GIS software, in combination with the management of another information layer to be synthesized in large quantities a variety of data, can get the data more effectively. GIS, to serve them better farmers and breeding community, provides a powerful means of agriculture. The proposed SGR and GIS determine the production, estimate the yield and area extraction procedure, several different satellite data sources, and use the climate data crop model and data.
ISSN:0141-9331
DOI:10.1016/j.micpro.2021.103893