On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

Abstract

Abstract:The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R c )=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

Study on discrimination of white tea and albino tea based on near‐infrared spectroscopy and chemometrics

“…The greater the squared Mahalanobis distance between two given groups, the greater the spectral differences between them. The unknown sample is classified by computing its Mahalanobis distance from each class center of gravity, as described elsewhere . If it is close to the center of gravity of its group, it is ‘correctly classified’.…”

“…The unknown sample is classified by computing its Mahalanobis distance from each class center of gravity, as described elsewhere. 30,31 If it is close to the center of gravity of its group, it is 'correctly classified'. In other cases, where the distance to the center of gravity of its group is larger than that to the center of gravity of another group, then it is 'poorly classified' and will be reassigned to the other group.…”

Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges

“…An approach carried out on two tomato varieties cultivated in a controlled environment and assessed by a USB2000 spectrometer equipped with a grating, a detector (350~1100 nm, resolution 1-5 nm), and an optical fiber cable is reported in [104]. The discriminant analysis performed on various measurements in different top canopy leaves allowed development of a predictive model able to separate the two cultivars with accuracy up to 93% [104].…”

“…An approach carried out on two tomato varieties cultivated in a controlled environment and assessed by a USB2000 spectrometer equipped with a grating, a detector (350~1100 nm, resolution 1-5 nm), and an optical fiber cable is reported in [104]. The discriminant analysis performed on various measurements in different top canopy leaves allowed development of a predictive model able to separate the two cultivars with accuracy up to 93% [104]. In a second study, the discrimination of nontransgenic and transgenic tomato lines, antisensed for the ethylene receptor genes LETR1, was carried out using an FT-NIR spectrometer system (Thermo Electron Corp., Madison, WI, USA) fitted with an optic fiber cable and a 50 W quartz-halogen light source [105].…”

Assessing the potential of remote sensing to discriminate invasive <i>Seriphium plumosum</i> from grass

“…These bands have been widely used in studies focusing on remote sensing of vegetation characteristics (e.g. Calvini et al, 2015;Dale et al, 2011;Glenn et al, 2008;Manevski et al, 2011;Pflugmacher, 2007;Tuxen et al, 2008;Xu et al, 2009). Simulations were done for all individuals of S. plumosum and grass, resulting in two separate pools per spectral band.…”