Apple Bruise Grading Using Piecewise Nonlinear Curve Fitting for Hyperspectral Imaging Data

Abstract

Apple fruits can be easily damaged, and bruises occur on peels during harvest, transportation and storage, which could decrease fruit quality. This paper proposed an apple bruise grading method based on hyperspectral imaging (HSI). The spectral information of sound apples (Yantai Fuji 8) was first captured using a hyperspectral reflectance imaging device (386-1016 nm). These apples were then mechanically damaged by the same impact forces, and the bruised regions were exposed to room temperature for at most 120 min. The spectral data of the bruised apples at four different exposure times (30 min, 60 min, 90 min and 120 min) were obtained. The spectral data were preprocessed using Procrustes analysis (PA) to enable a more diverse distribution of the spectra among different patterns. To both accurately maintain the spectral information of different patterns and reduce the dimensions of the spectra, piecewise nonlinear curve fitting (PWCF) was presented using the least squares algorithm, where the resultant fitting coefficients from different spectral intervals were catenated into a low-dimension feature descriptor. The feature descriptors were then fed to an error-correction output coding-based support vector machine (ECOC-SVM) to grade the bruised apples. To further evaluate the performance of the presented PWCF, conventional algorithms, including the successive projections algorithm (SPA), genetic algorithm (GA), principal component analysis (PCA) and kernel principal component analysis (KPCA), were introduced for comparison. Experimental results showed that the proposed method obtained the best grading accuracy (97.33%) compared to the other methods.

Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging

“…Scanning systems offer the advantage of producing images that can be easily extracted from raw data captured where high-speed acquisition is not required, for example, in agricultural measurements. Rapid data collection is often not necessary because vegetation changes over time scales of tens of minutes or hours [16]. Such systems are often attached to drones to produce hyperspectral images of large areas such as fields to assess characteristics of large numbers of plants simultaneously [17].…”

“…Such systems are often attached to drones to produce hyperspectral images of large areas such as fields to assess characteristics of large numbers of plants simultaneously [17]. Lab-based systems are also used to assess individual plant properties, such as damage and/or deterioration in fruits and other fresh produce for non-destructive quality control and assessment [9,16]. Wider availability of low-cost, easy to operate systems that require minimal post-processing would enable better individual plant monitoring during growth and improved quality control in food distribution.…”

Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

“…As the browning degree of the bruised area of samples increasing with the storage time, the browning degree changes more obvious at the beginning, the browning degree is close to the maximum value at 24 h, so that the browning degree doesn't change much at 48 h comparing with 24 h. Two distinct absorption valleys exist in all types of spectra. The absorption valley in the S1 region (680-720 nm) is caused by the chlorophyll in yellow peaches, the other in the S2 region (980-1020 nm) is caused by the O-H bond vibrations of water and sugar components (Tang et al 2020).…”

“…Based on raw data and pre-processed spectra, a partial least squares discriminant analysis (PLS-DA) classification model was established; then, various variable selection methods were used to select the feature variables; and finally, a PLS-DA model based on the feature variables was developed; the results showed that the hyperspectral imaging technology combining with PLS-DA could distinguish bruise's Lingwu long jujube at different storage times. Tang et al (2020) exposed the bruised area of the apple at room temperature for different periods, and spectral data of these samples were collected based on hyperspectral technology; then piecewise nonlinear curve fitting (PWCF) was used to extract the feature descriptors; finally, the feature descriptors were fed into an error correction output coding-based support vector machine (ECOC-SVM). The experimental results showed that the method effectively classified the storage time of bruised apples.…”

Support Vector Machine and YOLO for a Mobile Food Grading System

“…The selected features for training and testing the model include color, texture, and shape features. In [19], they proposed a piecewise nonlinear curve fitting (PWCF) procedure to maintain and present the spectral features of the hyperspectral images, and the error-correcting output code-based support vector machine (ECOC-SVM) was adopted to address the apple bruise grading problem. In [20], they utilized a Radio-frequency identification (RFID) tag as an electromagnetic transducer to convert the chemical-physical change of avocados into a modulation of the electromagnetic parameters when the avocados are ripening.…”