Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit

Abstract

IntroductionFresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique.MethodsA total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification.Results and discussionThe accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications.

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

“…Food defects detected with HSI include degradation of spinach leaves [31], mechanical damage on mushrooms [32], and foreign objects in dried seaweed [33]. Fresh fruits inspected with HSI include apples [34], [35], nectarines [36], peaches [37], jujubes [38], tart cherries [39], pears [40], pomegranates [41], and mangoes [42], [43]. As hyperspectral cameras become more compact and inexpensive [44], [45], use of them in conjunction with UAV's will become more widespread.…”

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

“…Li et al (2019) proposed an improved watershed segmentation algorithm based on hyperspectral imaging and principal component analysis, which can effectively classify sound apples and early rotten apples with detection accuracy of 99% and 100%, respectively. Okere et al (2023) used a hyperspectral imaging device to obtain pomegranate images and classified the bruises in pomegranate fruits based on the average spectrums of the regions of interest of sample hypercubes and a two‐layer feed‐forward artificial neural network. The accuracy of different bruise severity classification ranges from 80% to 96.7%.…”