Research on the application of data-driven biophilic design in human-plant interaction

This paper provides insights into the application of data-driven biophilic design in human-computer interaction systems, with a particular focus on the effects of environmental factors such as light, temperature and humidity on plant growth. A three-level structure based on data acquisition, biometric modeling, and human-computer interaction is proposed. On this framework, deep learning methods such as convolutional neural networks and SVM are utilized to analyze and predict the physiological responses of plants in different environments. The integration of this method enables the system to accurately categorize the growth condition of plants and provide real-time monitoring and feedback. Numerous experiments have shown that appropriate light intensity, suitable temperature and humidity conditions are favorable to plant growth and development. The research of this project will provide a theoretical basis for ecological education, environmental management and sustainable development in China.