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Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

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Journal of Forestry Research 2023年第1期34卷 269-282页

作者： Rafaela Lancas Gomes Marília Caixeta Sousa Felipe Girotto Campos Carmen Sílvia Fernandes Boaro josé raimundo de souza passos Gisela Ferreira Bioestatistic Plant BiologyParasitology and Zoology DepartmentBioscience Institute(IBB)Universidade Estadual Paulista(UNESP)BotucatuSao PauloBrazil

Nitrogen(N)monitoring is essential in nurseries to ensure the production of high-quality *** spectroscopy(NIRS)is an instantaneous,nondestructive method to monitor *** data such as NIRS can also provide the basis for developing a new vegetation spectral index(VSI).Here,we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emargiaata and developed a new VSI for this *** were grown in a hydroponics system with 0,2.75,5.5or 11 mM N for 45 *** we measured gas exchange,chlorophylla fluorescence,and pigments in leaves;analyzed complete leaf nutrients,and recorded spectral data for leaves at 966 to 1685 nm using *** a statistical learning approach,the dimensionality of the spectral data was reduced,then models were generated using two classes(N deficiency,N)or four classes(0,2.75,5.5,11 mM N).The best combination of techniques for dimensionality reduction and classification,respectively,was stepwise regression(PROC STEPDISC)and linear discriminant *** was possible to detect N deficiency in seedlings leaves with 100%precision,and the four N concentrations with93.55%accuracy before photosynthetic damage to the plant ***,NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of ***.

关键词： Mineral nutrition of plants Near-infrared spectroscopy Spectral vegetation index Digital signature Statistical learning Fluorescence of chlorophylla

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