Hyperspectral imaging to classify and monitor quality of agricultural materials

Sivakumar, Mahesh; Jayas, Digvir S.; Paliwal, Jitendra; White, N. D. G.

doi:10.1016/j.jspr.2015.01.006

Cited by 175 publications

(73 citation statements)

References 54 publications

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“…The development of an MSI system can reduce these drawbacks, mainly due to its possibility to select the most significative wavelengths (from 3-15) in order to predict the physicochemical attributes of interest [137]. MSI has several advantages compared to HSI (i.e., faster scan rate, feasibility of on-line application in the food processing industry, less computer memory required to acquire and process the images) [138]. The drawbacks are related to the device lacking flexibility.…”

Section: Hyper-and Multi-spectral Imagingmentioning

confidence: 99%

“…However, in all the studies found in the literature, only color and moisture content were investigated with these devices. In other fields instead, these devices were successfully used (e.g., in quality and safety [15,116,150], as well as in post-harvest control of fresh fruits and vegetables using NIR [19,138,162,163] and HSI [164,165] to monitor a broad range of physicochemical changes such as: sugar content [166], protein content [167,168], starch index [135] and several related nutritional compounds [169,170]. For this reason, it seems reasonable to develop a model able to detect the same compounds and their changes during the drying process.…”

Section: Quality Control Of Vegetables During Dryingmentioning

confidence: 99%

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Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

et al. 2017

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An overview is given regarding the most recent use of non-destructive techniques during drying used to monitor quality changes in fruits and vegetables. Quality changes were commonly investigated in order to improve the sensory properties (i.e., appearance, texture, flavor and aroma), nutritive values, chemical constituents and mechanical properties of drying products. The application of single-point spectroscopy coupled with drying was discussed by virtue of its potentiality to improve the overall efficiency of the process. With a similar purpose, the implementation of a machine vision (MV) system used to inspect foods during drying was investigated; MV, indeed, can easily monitor physical changes (e.g., color, size, texture and shape) in fruits and vegetables during the drying process. Hyperspectral imaging spectroscopy is a sophisticated technology since it is able to combine the advantages of spectroscopy and machine vision. As a consequence, its application to drying of fruits and vegetables was reviewed. Finally, attention was focused on the implementation of sensors in an on-line process based on the technologies mentioned above. This is a necessary step in order to turn the conventional dryer into a smart dryer, which is a more sustainable way to produce high quality dried fruits and vegetables.

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Section: Hyper-and Multi-spectral Imagingmentioning

confidence: 99%

Section: Quality Control Of Vegetables During Dryingmentioning

confidence: 99%

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

et al. 2017

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“…As a result, the HS imagery has very high spectral resolution, and is a three-dimensional data cube, of which two spatial dimensions contain the space information, and one spectral dimension at each pixel includes the high-dimensional reflectance vectors [2,3]. Such HS image with abundant spectral information has been widely utilized in many domains, such as military surveillance [4], environmental monitoring [5], mineral exploration [6,7], and agriculture [8,9]. However, due to the constraints of technical difficulties and budget, the HS image usually has low spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Structure Tensor-Based Algorithm for Hyperspectral and Panchromatic Images Fusion

Lei

et al. 2018

Remote Sensing

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Abstract:Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information.

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“…Different supervised and unsupervised methods followed by feature extraction procedures are generally used to extract important spatial and spectral patterns. The main steps required to analyze hyperspectral images include pre-processing of data, dimensionality reduction, enhancement of spectral responses, and component detection or classification (Mahesh et al, 2015). HSI with advanced chemometrics methods is nowadays gaining in importance for detecting adulteration in various food products.…”

Section: Introductionmentioning

confidence: 99%

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

Mishra

Cordella

Rutledge

et al. 2016

Journal of Food Engineering

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a b s t r a c tIn recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained.

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Hyperspectral imaging to classify and monitor quality of agricultural materials

Cited by 175 publications

References 54 publications

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

Structure Tensor-Based Algorithm for Hyperspectral and Panchromatic Images Fusion

Application of independent components analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration

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