Size Estimation of Tomato Fruits Based on Spectroscopic Analysis

Abstract

This study used visible and near-infrared (VIS-NIR) spectroscopy for size estimation of tomato fruits of three cultivars. A mobile, fibre-type, VIS-NIR spectrophotometer (AgroSpec, Tec 5, Germany) with spectral range of 350-2200 nm, was used to measure reflectance spectra of on-vine tomatoes growing from July to September 2010. Spectra were divided into a calibration set (75%) and an independent validation set (25%). A partial least squares regression (PLSR) with leave-one-out cross validation was adopted to establish calibration models between fruit diameter and spectra. Furthermore, the latent variables (LVs) obtained from PLS regression was used as input to back-propagation artificial neural network (BPANN) analysis. Result shows that the prediction of PLSR model can produce good performance with coefficient of determination (R2) of 0.82, root-mean-square error of prediction (RMSEP) of 4.87 mm and residual prediction deviation (RPD) of 2.35. Compared to the PLSR model, the PLS-BPANN model provides considerably higher prediction performance withR2of 0.88, RMSEP of 3.98 mm and RPD of 2.89. It is concluded that VIS-NIR spectroscopy coupled with PLS-BPANN can be adopted successfully for size estimation of tomato fruits.

Applications of spectroscopic-based techniques for quality and safety evaluation of tomatoes: A mini-review

“…Furthermore, the conventional method called "visual grading" for size measurement is a manual operation that is tedious, laborintensive, and subjective [13]. To overcome these drawbacks, Yang et al [14] established the application of spectroscopic-based technology for rapid measurement of tomato size and the approach was able to determine the fruit quality based on size.…”

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

“…However, non-destructive methods have demonstrated a great interest over traditional methods as they provide several advantages such as real-time assessment, multiple simultaneous measurements, and real-time decision-making. Many of these techniques have been proposed to determine the optimum harvest date of fruits, including fluorescence imaging (Cerovic et al, 2009), colorimetry (Baltazar et al, 2008), computed tomography scan (Kotwaliwale et al, 2012), machine vision (Sabzi et al, 2019), visible and near-infrared (Vis/NIR) spectroscopy (Yang et al, 2011), multi-spectral imaging , and hyperspectral imaging (Su et al, 2021). These non-destructive techniques, especially those based on optical and imaging properties, have proven to be a very useful and powerful tool for estimating the ripening stages of fruits.…”