Quality inspection of nectarine based on hyperspectral imaging technology

Huang, Feng; Liu, Yanhong; Sun, Xinyi; Yang, Hua

doi:10.1080/21642583.2021.1907260

Cited by 11 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for hyperspectral image for fruit SSC determination, the spectral features, image features, and the fusion of spectral features and image features have also been used (Fan et al, 2016;Weng et al, 2020;Huang et al, 2021;Pang et al, 2021). In addition to 1D spectra of 1D ResNet, 3D hyperspectral images with more spectral and image features could be used to establish 3D ResNet model, without the use of pre-extracted spectral features and image features.…”

Section: Discussionmentioning

confidence: 99%

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Zhang

Yan

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Zhang

Yan

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Hyperspectral imaging technology was applied for the quality detection of nectarines through machine learning models (Huang et al 2021 ). Soluble solid content was measured as an internal quality index while the external quality indices included: intact, cracked, and dark damage.…”

Section: Non-destructive Analyses For Monitoring Chilling Injurymentioning

confidence: 99%

“…Determination coefficients and root means squared errors predicted SSC with best fit for LS-SVM. LS-SVM was indicated an excellent potential model to predict and discriminate the internal and external quality of nectarines (Huang et al 2021 ).…”

Section: Non-destructive Analyses For Monitoring Chilling Injurymentioning

confidence: 99%

Biochemical and molecular changes in peach fruit exposed to cold stress conditions

Franzoni,

Spadafora,

Sirangelo

et al. 2023

Mol Horticulture

View full text Add to dashboard Cite

Storage or transportation temperature is very important for preserving the quality of fruit. However, low temperature in sensitive fruit such as peach can induce loss of quality. Fruit exposed to a specific range of temperatures and for a longer period can show chilling injury (CI) symptoms. The susceptibility to CI at low temperature varies among cultivars and genetic backgrounds. Along with agronomic management, appropriate postharvest management can limit quality losses. The importance of correct temperature management during postharvest handling has been widely demonstrated. Nowadays, due to long-distance markets and complex logistics that require multiple actors, the management of storage/transportation conditions is crucial for the quality of products reaching the consumer.Peach fruit exposed to low temperatures activate a suite of physiological, metabolomic, and molecular changes that attempt to counteract the negative effects of chilling stress. In this review an overview of the factors involved, and plant responses is presented and critically discussed. Physiological disorders associated with CI generally only appear after the storage/transportation, hence early detection methods are needed to monitor quality and detect internal changes which will lead to CI development. CI detection tools are assessed: they need to be easy to use, and preferably non-destructive to avoid loss of products. Graphical Abstract

show abstract

“…Contaminants detected with HSI include traces of nuts in wheat flour ( Zhao et al., 2018 ), foreign objects in cut vegetables ( Cho, 2021 ), contaminants on meat ( Gorji et al., 2022 ) and fecal and ingesta contaminants on poultry carcasses ( Park et al., 2007 ). Fresh fruits inspected with HSI include mandarins ( Zhang et al., 2020 ), nectarines ( Huang et al., 2021 ), jujubes ( Pham and Liou, 2022 ), citrus ( Gómez-Sanchis et al., 2008 ; Qin et al., 2013 ; Kim et al., 2014 ), tart cherries ( Qin and Lu, 2005 ), pears ( Lee et al., 2014 ), and mangoes ( Rivera et al., 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Citrus disease detection using convolution neural network generated features and Softmax classifier on hyperspectral image data

et al. 2022

View full text Add to dashboard Cite

Identification and segregation of citrus fruit with diseases and peel blemishes are required to preserve market value. Previously developed machine vision approaches could only distinguish cankerous from non-cankerous citrus, while this research focused on detecting eight different peel conditions on citrus fruit using hyperspectral (HSI) imagery and an AI-based classification algorithm. The objectives of this paper were: (i) selecting the five most discriminating bands among 92 using PCA, (ii) training and testing a custom convolution neural network (CNN) model for classification with the selected bands, and (iii) comparing the CNN’s performance using 5 PCA bands compared to five randomly selected bands. A hyperspectral imaging system from earlier work was used to acquire reflectance images in the spectral region from 450 to 930 nm (92 spectral bands). Ruby Red grapefruits with normal, cankerous, and 5 other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. A novel CNN based on the VGG-16 architecture was developed for feature extraction, and SoftMax for classification. The PCA-based bands were found to be 666.15, 697.54, 702.77, 849.24 and 917.25 nm, which resulted in an average accuracy, sensitivity, and specificity of 99.84%, 99.84% and 99.98% respectively. However, 10 trials of five randomly selected bands resulted in only a slightly lower performance, with accuracy, sensitivity, and specificity of 98.87%, 98.43% and 99.88%, respectively. These results demonstrate that an AI-based algorithm can successfully classify eight different peel conditions. The findings reported herein can be used as a precursor to develop a machine vision-based, real-time peel condition classification system for citrus processing.

show abstract

Quality inspection of nectarine based on hyperspectral imaging technology

Cited by 11 publications

References 14 publications

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

Biochemical and molecular changes in peach fruit exposed to cold stress conditions

Citrus disease detection using convolution neural network generated features and Softmax classifier on hyperspectral image data

Contact Info

Product

Resources

About