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.
Highlights
Apple is one of the most popular fruits around the world, and its annual production amounts to 80 million tons [1]
Liu et al [24] compared the performance of the detection of defects in hawthorn using different preprocessing algorithms, such as standard normal variable transform (SNV), SavitzkyGolay smoothing (SGS), median filtering and multiplicative scatter correction (MSC), and the results indicated that the SNV method was most suitable for the detection of hawthorn defects
According to the findings reported by Dietterich et al [35], this framework encoded each apple pattern into an error-correcting codeword with 10 bits and transmitted each bit via a separate learning process using the support vector machine (SVM) algorithm, which could be able to recover the potential prediction errors likely introduced by a set of finite training instances and poor choice of input features
Summary
Apple is one of the most popular fruits around the world, and its annual production amounts to 80 million tons [1]. Apple fruits can be damaged during the process of harvest, transportation and postharvest storage, which might cause obvious bruising of the apples and decrease the fruit quality [2, 3]. Fruit sorting is usually considered to filter out defective apples. Accurate bruise grading, especially the early detection and grading is an important procedure involved in an apple sorting system. Typical fruit bruise detection and grading methods are based on sensory evaluation [4,5,6] and physicochemical analysis [7,8,9]. Sensory evaluation-based methods heavily depend on personal subjective experience, leading to low reliability and poor repeatability, while physicochemical analysis-based methods usually require
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