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

Abstract

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.

Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine

“…The leaves were de-enzymed under 105°C for 30 min and dried to a constant weight under 75°C, then used to measure total N using the Kjeldahl method [22] .…”

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

“…Spectral polarization imaging (SPI) aims to obtain four-dimensional (4D) representation data of target scenes, consisting of two-dimensional (2D) spatial information, onedimensional spectral information, and the polarization state information represented by full-Stokes parameters [1,2]. Due to the its ability to reveal and retrieve target information, e.g., material composition, surface texture and characteristic details, SPI has been extensively applied in many night-vision-related fields, such as anomaly detection, field reconnaissance, night navigation, and so on [3][4][5][6][7][8][9][10].…”

Vigor Detection for Naturally Aged Soybean Seeds Based on Polarized Hyperspectral Imaging Combined with Ensemble Learning Algorithm

“…In the field of military defense, PHI technology can be used to suppress the background, so as to highlight the intended objects in order to conduct rapid exploration of camouflaged military targets [16]. In the field of agriculture, it can not only detect defects in crops [17] and diseases in crops [18], but also detect the content of sugar and other substances in crops [19]. These research results all confirm the advantages, feasibility, and universality of PHI technology and provide a theoretical basis for the method of this study.…”