Study on Analysis of Environmental Parameters and Ecological Prediction of Flowers Based on Machine Learning

This paper uses machine learning and related technologies to process, analyze and study the ecological environment parameters of flowers, and makes ecological predictions on them. Taking orchids as an example, data collection and processing were conducted to analyze and study the correlation and distribution density of environmental parameters such as temperature, moisture, conductivity, pH value, nitrogen, phosphorus, potassium, and fertility, as well as their impact on leaf color parameters. And our research team gradually improve the data collection method to increase the accuracy of the original data. According to the obtained statistical data, a regression model is established for modeling analysis and ecological prediction. Further this paper improved the coefficients, reduced errors, and enhanced the actual performance of the data model. The research methods, content, and predictive information presented in this paper provide valuable practical applications for the technological development and intelligent management in the field of flowers.