银杏雄花序光谱特征时序变化分析及氮含量估测OA北大核心CSTPCD

[Objective]Nitrogen is a key element that significantly impacts the growth and development of ginkgo trees.As a dynamically active physiological organ,the nitrogen content in male inflorescences of ginkgo trees has a direct impact on its fertilization and senescence processes.However,previous research has primarily focused on the spectral estimation of nitrogen content in leaves of horticultural crops like fruit trees.Few studies have utilized spectral techniques to monitor the nitrogen content of ginkgo male inflorescences.Therefore,the objective of this study is to establish an optimal estimation model for nitrogen content in ginkgo male inflorescences,thus enabling rapid and non-invasive monitoring of nitrogen content fluctuations.[Method]This study focused on the male inflorescences of ginkgo trees,quantitatively analyzing the dynamic changes of nitrogen content in the inflorescences over time.A portable spectrometer was used to simultaneously acquire high-resolution spectral reflectance of the inflorescences,and nitrogen-related spectral indices were extracted.Through correlation analysis and the All-subsets Regression(ASR)algorithm,target spectral indices were selected.Subsequently,models were developed based on Multiple Linear Regression(MLR)and Random Forest Regression(RFR),and the Akaike Information Criterion(AIC)was employed to assess the fit of the MLR model.Finally,the accuracy of the models was tested using leave-one-out cross-validation.[Result]The results indicate that the nitrogen content in ginkgo male inflorescences exhibited a gradual decreasing trend over time.Among numerous spectral indices,sensitivity analysis and the All-subsets Regression algorithm identified the spectral indices NI_Ferwerda,MCARI/OSAVI,and TCARI as being particularly sensitive to nitrogen content in ginkgo male inflorescences(adjusted R2=0.73).The performance of the Multiple Linear Regression model based on these selected spectral parameters(CV-R2=0.70)generally outperformed the Random Forest Regression model(CV-R2=0.63).[Conclusion]The Multiple Linear Regression model constructed based on the selected spectral indices demonstrated a favorable estimation performance,accurately predicting the nitrogen content in ginkgo male inflorescences.Furthermore,this study offers theoretical and technical supports for the effective monitoring of nitrogen nutrition in the male inflorescences of ginkgo trees.