Hyperspectral Classification for Identifying Decayed Oranges Infected by Fungi

Abstract

Fast and nondestructive detection of early decay caused by fungal infection in citrus fruit was a challenging task for the citrus industry during the postharvest fruit processing. In general, workers relied on the ultraviolet induction fluorescence technique to detect and remove the decayed citrus fruits in fruit packing houses. However, this operation was harmful for human health, and was also very inefficient. In this study, navel oranges were used as research object. A novel method combining with hyperspectral imaging technology in the wavelength region between 400 and 1100 nm wavelength was proposed to solve this problem. First, normalization approaches were applied to decrease the variation of spectral reflectance intensity due to natural curvature of navel orange surface. Then, the spectral data of regions of interest (ROIs) from normal and decayed tissues was analyzed by principal component analysis (PCA) for investigating the performance of visible and near infrared (Vis-NIR) hyperspectral data to discriminate these two kinds of tissues. Next, six characteristic wavelength images were obtained by analyzing the loadings of the first principal component (PC1). And, a multispectral image was established by using the corrected six characteristic wavelength images. On basis of the multispectral image, pseudo-color image processing with intensity slicing was utilized to produce a two-dimensional color image with clear contrast between decayed and normal tissues. Finally, an image segmentation algorithm by combining the pseudo-color processing method and a global threshold method was proposed for fast identification of decayed navel oranges. For 240 independent samples, the success rates were 100 and 97.5% for decayed navel oranges infected by Penicillium digitatum and normal navel oranges, respectively. In particular, the proposed algorithm was also applied to detect the decayed navel oranges infected by Penicillium italicum (samples not used for the development of algorithm) and obtained a 91.7% identification accuracy, indicating a well generalization ability and actual application value of the proposed algorithm.

Postharvest Technologies of Fresh Citrus Fruit: Advances and Recent Developments for the Loss Reduction during Handling and Storage

“…By deploying the above-mentioned spectroscopy-and image-based analytical techniques (NIR/VIS, HIS, Raman and NMR) coupled with proper image processing techniques, it has been possible to correlate optical properties with several parameters to detect or predict the behaviour of many quality parameters, mainly chemical and physicochemical, and, in some works, the extent, at an early stage, of pathological decay in oranges [258][259][260][261][262] or freezing damage in sweet lemons [263] and oranges [264,265].…”

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka–Munk spectral data

“… 55 The wave valley at 995 nm is due to the O–H bond in the water molecule. 56 The more severely damaged yellow peaches are, the lower the spectral reflectance is. This indicates that spectral information can reflect the damage degree of yellow peaches.…”

Study on the Quantitative Assessment of Impact Damage of Yellow Peaches Using the Combined Hyperspectral Technology and Mechanical Parameters

“…Figure 5 shows the average spectra of yellow peach samples at six different collision angles. As can be seen from Figure 6, all the average spectral curves show distinct troughs at 704 nm and 995 nm, where the trough at 704 nm is due to the vibrational contraction of the C-H bond in the carbohydrate, and the trough at 995 nm is due to the O-H bond in the water molecule [35]. Specifically, impact damage can cause tissue damage and cell rupture in yellow peaches.…”