Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging

Li, Jiangbo; Huang, Wenqian; Tian, Xi; Wang, Chaopeng; Fan, Shuxiang; Zhao, Chunjiang

doi:10.1016/j.compag.2016.07.016

Cited by 126 publications

(41 citation statements)

References 36 publications

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“…Recently, more advanced research has been performed in laboratories to objectively quantify and certify the quality attributes of food products based on various spectroscopic technologies (Damez and Clerjon ; Su and others ). Among them, Fourier transform infrared (FT‐IR) spectroscopy, Raman spectroscopy, and hyperspectral imaging (HSI) are considered to be very promising tools for quality analysis due to their noninvasive and rapid detection features, as well as their ability to explore food materials with minimal sample preparation (Lu and others ; Alexandrakis and others ; Lohumi and others ; Zhang and others ; Lee and Herrman ; Li and others ; Su and Sun ; Xie and others ; Baum and others ; Su and others ; Su and others ; Zhao and others ). Although several reviews have been published on these novel techniques, they have only focused on one or two spectroscopic methods that has been applied to just a handful of food products, such as chicken meat (Xiong and others ), fruits (Li and others ), red meats (Xiong and others ), muscle foods (Herrero ; Cheng and others ), and liquid foods (Wang and others ).…”

Section: Introductionmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

134

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Fourier transform infrared (FT-IR) and Raman and hyperspectral imaging (HSI) techniques have emerged as reliable analytical methods for effectively characterizing and quantifying quality attributes of different categories of powdery food products (such as milk powder, tea powder, cocoa powder, coffee powder, soybean flour, wheat flour, and chili powder). In addition to the ability for gaining rapid information about food chemical components (such as moisture, protein, and starch), and classifying food quality into different grades, such techniques have also been implemented to determine trace impurities in pure foods and other properties of particulate foods and ingredients with avoidance of extensive sample preparation. Developments of corresponding quality evaluation systems based on FT-IR, Raman, and HSI data that measure food quality parameters and ensure product authentication, would bring about technical and economic benefits to the food industry by enhancing consumer confidence in the quality of its products. Accordingly, a comprehensive review of the mushrooming spectroscopy-based FT-IR, Raman, and HSI literature is carried out in this article. The spectral data collected, the chemometric methods used, and the main findings of recent research studies on quality assessments of powdered materials are discussed and summarized. Providing a review in such a flourishing research field is relevant as a signpost for future study. The conclusion details the promise of how such noninvasive and powerful analytical techniques can be used for rapid and accurate determinations of powder quality attributes in both academical and industrial settings.

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Section: Introductionmentioning

confidence: 99%

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Sun

2017

Comp Rev Food Sci Food Safe

134

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“…In addition to machine vision technology, another commonly used method is spectroscopy, which is a fast and nondestructive method. Unfortunately, the distribution of the chemical composition or tissue properties of a sample cannot be determined using this technique because conventional spectroscopy systems can only provide one spectrum of a small area of the target sample without giving any spatial information . Pulsed‐phase thermography (PPT) has also recently been applied in early bruise detection in apples.…”

Section: Introductionmentioning

confidence: 99%

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Tan

Sun

Yang

et al. 2018

Journal of Chemometrics

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The ability to determine if an apple is bruised and to provide quantitative and objective descriptions of the degree of bruising is not the only important basis for assessing apple quality but also has significance for improving the postharvest handling of apples. In this study, segmented principal component analysis for hyperspectral images in the spectral range of 401 to 1037 nm was carried out, and seven characteristic wavelengths were selected based on the weight coefficients of the principal component images. By using the principal component analysis operations with the selected wavelengths and image processing methods, an accurate recognition algorithm for apple bruises was proposed. For 40 intact samples and 160 bruised samples, the average correct recognition rate was 99.1%. Moreover, this paper obtained the average spectra of 157 segmented bruised regions by applying a binary mask. A characteristic wavelength selection method that combines competitive adaptive reweighted sampling with correlation coefficient methods and supports vector machine modeling methods based on grid parameter optimization was put forward for the classification and identification of the bruising degrees of apples. The results showed that the classification accuracy was as high as 97.5% for the test set. Overall, this study demonstrated that hyperspectral imaging technology can be used to accurately and effectively identify early bruises and determine the bruising degree of apples, which provides a new method for on‐line, nondestructive detection, and grading of early bruises in apples.

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“…Existing studies utilizing hyperspectral imaging sensors are sparse compared with those utilizing non-imaging hyperspectral sensors. Field ASD hyperspectral radiometers have long been used in agricultural applications [16], such as the inversion of crop parameters [17][18][19], crop disease and pest monitoring [20][21][22], etc. Pushbroom hyperspectral sensors have been developed and deployed for agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

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Abstract:Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed Remote Sens. 2017, 9, 642; doi:10.3390/rs9070642 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 642 2 of 21 method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

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Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging

Cited by 126 publications

References 36 publications

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A Review

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

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