Neural network-based urban green vegetation coverage detection and smart home system optimization

Artificial neural networks are usually simplified to neural networks in a broad sense, and have been a hot topic in the field of intelligence applications since 1980. Neural network is a non-linear system that simulates the brain information processing algorithm. It has the functions of energy distributed information storage, parallel processing, and adaptive learning. Its structure is to connect and construct specific models through simple neural processing devices to form a network of multiple connection methods. In the past 10 years, the development speed of neural networks has been in-depth and extensively developed through intelligent application software and innovative technologies. It has been able to solve many practical projects in the fields of crustal learning, pattern recognition, signal processing, modeling technology, and system control. Since the reform and opening up, the area of urban green space has continued to increase, which has become one of the characteristics of major changes in the urban land structure of modern China. The ratio of green space in construction areas is one of the Chinese government’s benchmarking indicators for the level of urban green space construction, and it is an important data expression of the urban human settlement level and living environment. In China’s new urbanization process, increasing the ratio of green space in construction areas is an important requirement. At the same time, with the rapid development of modern science and technology, mankind has entered the information age from the industrial age. People who are pursuing a better living environment and quality life have appeared in smart home systems. The smart home system is a comprehensive management system that can connect various sensors, electronic and electrical equipment through the network to realize home environment monitoring, home equipment automation, and residential safety early warning.