Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion

Polarized spectra–hyperspectral data fusion technique was used to estimate the soluble sugar (SS), total nitrogen (N), and their ratio (SS/N), of greenhouse tomato leaves. Fresh tomato leaves of five different growth stages (seedling, flowering, initial fruiting, mid-fruiting and picking stage) and five different nitrogen treatments (severe stress 25%, moderate stress 50%, mild stress 75%, normal 100%, and excess 150%) at every stage were collected for spectra acquisition and SS and N determination. Polarized reflectance spectra were acquired with a polarization reflectance spectrum spectro-goniophotometer system and four polarization degree features were extracted. Hyperspectral data were collected with a hyperspectral imaging system and four reflectance spectrum features and eight image features were extracted. Initially, models were built with polarization degree features, image features, and spectral features respectively. Linear and nonlinear fusion methods were comparatively used for modeling based on normalized data of the three sources. The results suggest that the performances of SS/N models are better than those of N and SS models, and the prediction capability of the Support Vector Machine (SVM) models of N and SS/N are superior to those obtained with single kind feature. This work indicates that the polarized spectrum-hyperspectral multidimensional information detecting method can feasibly judge the tomato nutrient stress conditions. Multi-features data fusion analysis technique can enhance the prediction accuracy of spectral diagnostics technology in precision agriculture. Keywords: polarized spectra, hyperspectral, soluble sugar (SS), total nitrogen (N), data fusion, tomato leaf DOI: 10.25165/j.ijabe.20201302.4280 Citation: Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra–hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197.