Hyperspectral imaging of intact bell peppers

Schmilovitch, Z.; Ignát, Tímea; Alchanatis, V.; Gatker, Janna; Ostrovsky, V.; Felföldi, József

doi:10.1016/j.biosystemseng.2013.07.003

Cited by 46 publications

(24 citation statements)

References 27 publications

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“…A previous study by Schmilovitch et al [21] developed a nondestructive method to evaluate chemical contents such as total soluble solids, total chlorophyll, carotenoid and ascorbic acid content in intact bell peppers by using hyperspectral imaging in the visible and near-infrared (550-850 nm) region. In addition, Mollazade et al [4] showed an image processing-based algorithm to evaluate the potential of HSI combined with artificial neural networks to spatial mapping of MC in tomato fruits in the spectral range of 400-1000 nm.…”

Section: Introductionmentioning

confidence: 99%

“…HSI can also provide more detailed or complete information, including internal structure characteristics, morphological information, and chemical composition of the sample, compared with a single-machine vision technology or spectroscopy analysis technology [9]. Now defined, the HSI technique has been applied to the nondestructive measurement of agricultural products such as determination of MC [4,[10][11][12][13][14], pH estimation [10,[15][16][17][18], soluble solid content (SSC) prediction, [10,11,16,[18][19][20][21] and so many other applications.…”

Section: Introductionmentioning

confidence: 99%

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Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

et al. 2017

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Abstract:The objective of this study was to develop a nondestructive method to evaluate chemical components such as moisture content (MC), pH, and soluble solid content (SSC) in intact tomatoes by using hyperspectral imaging in the range of 1000-1550 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and multivariate calibration models were built by using partial least squares (PLS) regression with different preprocessing spectra. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) first-derivative preprocessed spectra resulted in better performance for MC, pH, and the smoothing preprocessed spectra-based model resulted in better performance for SSC in intact tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (r pred ) of 0.81, 0.69, and 0.74 with root mean square error of prediction (RMSEP) of 0.63%, 0.06, and 0.33% Brix respectively. The full wavelengths were used to create chemical images by applying regression coefficients resulting from the best PLS regression model. These results obtained from this study clearly revealed that hyperspectral imaging, together with suitable analysis model, is a promising technology for the nondestructive prediction of chemical components in intact tomatoes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

et al. 2017

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“…[25][26][27] At the same time, full spectral regions may include wavelengths that contribute more collinearity, redundancy and noise than relevant information. 27 Recently, extensive studies have been conducted using different variable selection methods, such as regression coefficients, 8,16,21,28,29 variable importance in projection (VIP) [28][29][30][31] and the successive projections algorithm (SPA) 14,16,27,32,33 in pursuit of a robust calibration model for the quantitative or qualitative analysis of fruits and vegetables. However, to the best of our knowledge, no reports address the prediction of allicin and SSC in the quality assessment of garlic using the HSI technique in combination with different variable selection methods and robust calibration models.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models

2018

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“…Among the many challenges to be addressed, we can include appropriate image acquisition systems, lighting problems, color space definitions, mathematical issues, and the development of specific algorithms and synchronization tasks [13]. However, improvements in semiconductors, electronics, and software eventually brought the opportunity to implement image processing and colorimetric projects for various applications [11,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Building machines with the ability to see color as it does the human being has been a complex task for the scientific community and industry in recent years [8][9][10][11][12]. Among the many challenges to be addressed, we can include appropriate image acquisition systems, lighting problems, color space definitions, mathematical issues, and the development of specific algorithms and synchronization tasks [13].…”

Section: Introductionmentioning

confidence: 99%

Color Analysis and Image Processing Applied in Agriculture

Sandoval¹,

Rosas²,

Sandoval³

et al. 2018

Automation in Agriculture - Securing Food Supplies for Future Generations

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Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications.

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Hyperspectral imaging of intact bell peppers

Cited by 46 publications

References 27 publications

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models

Color Analysis and Image Processing Applied in Agriculture

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