Cargando…
Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks
Digitalization brings challenges and new opportunities to the development of landscape gardening, “smart gardening,” which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learn...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251076/ https://www.ncbi.nlm.nih.gov/pubmed/35795728 http://dx.doi.org/10.1155/2022/8307398 |
_version_ | 1784739957960605696 |
---|---|
author | Ji, Mingfei Lu, Jianrong Zhang, Xiaowei |
author_facet | Ji, Mingfei Lu, Jianrong Zhang, Xiaowei |
author_sort | Ji, Mingfei |
collection | PubMed |
description | Digitalization brings challenges and new opportunities to the development of landscape gardening, “smart gardening,” which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts “digital landscape design logic” to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools. |
format | Online Article Text |
id | pubmed-9251076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92510762022-07-05 Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks Ji, Mingfei Lu, Jianrong Zhang, Xiaowei Comput Intell Neurosci Research Article Digitalization brings challenges and new opportunities to the development of landscape gardening, “smart gardening,” which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts “digital landscape design logic” to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools. Hindawi 2022-06-26 /pmc/articles/PMC9251076/ /pubmed/35795728 http://dx.doi.org/10.1155/2022/8307398 Text en Copyright © 2022 Mingfei Ji et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ji, Mingfei Lu, Jianrong Zhang, Xiaowei Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title | Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title_full | Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title_fullStr | Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title_full_unstemmed | Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title_short | Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks |
title_sort | construction of a landscape design and greenery maintenance scheduling system based on multimodal intelligent computing and deep neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9251076/ https://www.ncbi.nlm.nih.gov/pubmed/35795728 http://dx.doi.org/10.1155/2022/8307398 |
work_keys_str_mv | AT jimingfei constructionofalandscapedesignandgreenerymaintenanceschedulingsystembasedonmultimodalintelligentcomputinganddeepneuralnetworks AT lujianrong constructionofalandscapedesignandgreenerymaintenanceschedulingsystembasedonmultimodalintelligentcomputinganddeepneuralnetworks AT zhangxiaowei constructionofalandscapedesignandgreenerymaintenanceschedulingsystembasedonmultimodalintelligentcomputinganddeepneuralnetworks |