Deciphering nitrogen concentrations in Metasequoia glyptostroboides : a novel approach using RGB images and machine learning

Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but traditional methods for N determination are labor-intensive,time-consuming,and destructive.In this study,we present a rapid,non-destructive method to predict leaf N concentration(LNC)in Metasequoia glyptostroboides plantations under N and phosphorus(P)fertilization using ML techniques and unmanned aerial vehicle(UAV)-based RGB(red,green,blue)images.Nine spectral vegetation indices(VIs)were extracted from the RGB images.The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine,ran-dom forest(RF),and multiple linear regression,gradient boosting regression and classification and regression trees(CART).The results show that RF is the best fitting model for estimating LNC with a coefficient of determination(R2)of 0.73.Using this model,we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P.Height,diameter at breast height(DBH),and crown width of all M.glyptostroboides were analyzed by Pearson correlation with the predicted LNC.DBH was significantly correlated with LNC under N treat-ment.Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient,scalable,and cost-effective method for LNC quantification.Future research can extend this approach to different tree species and different plant traits,paving the way for large-scale,time-efficient plant growth monitoring.