Enhancing Sustainable Urban Landscapes through AI-Driven Low-Carbon Plant Selection: A Novel Approach

Authors

  • Peiyuan Tao
  • Menghan Shen
  • Jianing Du
  • Peng Yao
  • Ming Shao

DOI:

https://doi.org/10.56028/aetr.8.1.696.2023

Keywords:

Low carbon; Carbon neutral; Plant landscape design; Natural language processing Deep learning.

Abstract

In the face of global environmental challenges, sustainable urban landscapes play a pivotal role in mitigating carbon emissions and fostering healthier cities. This paper presents a groundbreaking approach to low-carbon plant selection for landscape architecture, leveraging AI technology to address the limitations of existing language models in the field. Through the integration of professional databases, the introduction of judgment modules, low-carbon plant landscape recommendation modules, general dialogue modules, the use of prompt engineering technology, fine-tuning technology, our system offers precise, ecologically sound recommendations for low-carbon landscape designs.

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Published

2023-12-07