Monitoring Nitrogen Nutrition in Ginkgo Using Unmanned Aerial Vehicle RGB Imagery and the Gaussian Process Regression Model

Nitrogen nutrition monitoring is crucial in agriculture and forestry. With the development of Unmanned Aerial Vehicle (UAV) imaging technology, its application in nitrogen nutrition monitoring has gained attention. Traditional regression methods often struggle to accurately capture the nonlinear relationships between image features and nitrogen nutrition parameters. This study introduces Gaussian regression models to better model the relationship between UAV image features and nitrogen nutrition in Ginkgo. UAV RGB imagery of three-year-old Ginkgo biloba L. seedlings was used to extract nitrogen-related image features. Gaussian regression models were employed to select and model these features, creating regression models for nitrogen accumulation and nitrogen content in Ginkgo. The accuracy of the models was validated. Results indicated that the optimal canopy type for monitoring nitrogen accumulation in Ginkgo was the shadowed canopy, with the color feature BMR being the most important feature. For monitoring nitrogen content, sunlight and shadow canopy types were suitable, with BMR and b* being the key features. Gaussian regression demonstrated superior accuracy and robustness compared to traditional regression models. This study emphasizes the potential of Gaussian regression models to improve nitrogen monitoring through UAV imagery, offering valuable applications in precision agriculture and forestry management, particularly in supporting nitrogen fertilization and nutrition management for Ginkgo.