Landscape design and planning methods for plant protection based on deep learning and remote sensing techniques

Abstract

Landscape design and planning can play a role in beautifying the human living environment while improving the ecological protection of plants. However, it is difficult because it involves ornamental, ecological, social and other factors. Remote sensing data can be acquired and analyzed from vegetation distribution, ecological environment, land use and other information to support rational planning and design of plant protection landscape. Deep learning technology provides a powerful tool for the comprehensive use of remote sensing data to support plant protection landscape design. In this paper, a multiple dense network (MP-DenseNet) model is constructed based on Densenet to accomplish the task of classifying the vegetation cover of remotely sensed data, which provides data support for the design and planning of plant protection landscapes. The algorithm proposed in this paper is competitive in comparison with several models. Using the MP-DenseNet model, this paper categorizes the vegetation cover of the study area and proposes an overall planning scheme that balances plant protection and regional sustainable development.