Research on Key Technologies of Rural Landscape Design and Planning Based on Machine Vision

The concept of artificial intelligence was proposed in the 1950s, with the original intention of understanding and simulating human intelligence, aiming to achieve the simulation of human intelligence through computer algorithms. As a cutting-edge technology in the field of artificial intelligence, machine vision has made breakthrough progress and been widely applied in various fields in recent years, bringing new opportunities and solutions to rural landscape design. Machine vision mainly simulates the human visual system through computers, capable of quickly and accurately obtaining and analyzing a large amount of image information, thereby achieving precise recognition, measurement, and evaluation of target objects or scenes. This study aims to deeply explore the key technologies of rural landscape design and planning based on machine vision. Through systematic research and application practice of this technology, it provides scientific, efficient, and precise methods and means for rural landscape design, effectively solving many problems faced in current rural landscape design and planning, and comprehensively enhancing the quality and value of rural landscapes, promoting sustainable development in rural areas. This study proposes a new process and method system for rural landscape design and planning based on machine vision technology. By training a large number of labeled rural landscape images, the convolutional neural network (CNN) model has learned the feature patterns of different landscape elements, enabling accurate classification of new images. Utilizing deep learning algorithms to classify and identify targets in rural landscape images can automatically recognize different landscape elements such as farmland, forest land, water bodies, and buildings. By constructing a multi-technology integrated rural landscape design and planning system, the efficiency and quality of rural landscape design have been effectively improved, providing strong technical support for the implementation of the rural revitalization strategy.