Yield Estimation of Chilli Crop using Image Processing Techniques

Bhookya, Nageswararao Naik; Malmathanraj, R.; Palanisamy, P.

doi:10.1109/icaccs48705.2020.9074257

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…Lab color space has ability to separate luminosity from color, it includes all of the colors in spectrum which are outside of human perception, it is a good connection space between color spaces in conversion, more perceptually uniform than other color space, Lab color space is nonlinear, can isolate colors and it enhances color micro variations. Due to these reasons the Lab color space is very powerful than other color spaces (Ji et al 2019;Bhookya et al 2020). Kinnow fruit maturation is directly linked with peel pigmentations to represent color in green to yellow or orange range.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Prediction of fruit quality parameters using peel color in Citrus Reticulata L. fruit by multiple linear regression and artificial neural network approach

Ghanghas

Singh

Kumar

et al. 2022

Preprint

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Kinnow mandarin fruit color changes from deep green to completely orange during the period of peak maturity, which indicates if the fruit should be harvested. On the basis of the peel color, fifteen fruits were being selected for harvest, and their colors were measured with the chromameter which measures color in CIE-Lab color space. Green to Orange color fruits were divided into five regions (Green, Green-Yellow, Yellow, Yellow-Orange, Orange). The fruit samples in triplicate were used for all the laboratory analysis. The effects of color parameters i.e. L-value, a-value and b-value in different color regions on chemical parameters i.e. total soluble solids (TSS), titratable acidity (TA), fruit juice content (Juice %), maturity index (MI), ascorbic acid content (AA) and pH value of the fruit were studied using descriptive and correlation analysis. The model approaches i.e. multiple linear regression (MLR) and artificial neural network (multi-layer perceptron) (MLP-ANN) were implemented on the fruit sample data to predict the important fruit quality parameters which were prominent in determining the fruit color. The predicted data was plotted against the actual data using boxplot and scatter plot for both the models, the linearly best fitted and statistical significance was determined using R2, RMSE. Green and Green-Yellow colored region fruits have high in titratable acidity, ascorbic acid content and lower soluble sugars, maturity index and pH value. There was a steeper transition visible in TSS, juice content, maturity index, AA and pH value for the color shift from Yellow region to Yellow-Orange region. The better and optimal predictions were made with MLP-ANN for total soluble solids, titratable acidity and fruit juice content. The ANN model could be used in future for prediction of maturity indices of kinnow fruit in different maturity regions as per the consumer and market preferences.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Prediction of fruit quality parameters using peel color in Citrus Reticulata L. fruit by multiple linear regression and artificial neural network approach

Ghanghas

Singh

Kumar

et al. 2022

Preprint

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“…A Delta E value lower than 0.3 is not visible to the human eye, whereas a Delta E value of 2 is the minimum for visually detecting the difference between saturated colors. 14 Further, the visible total color difference between the unripe and ripe fruits is shown in the hue angle data obtained. However, based on the hue angle data obtained, the variation in the color of the ripe fruits shows that a pawpaw fruit may be ripe and still have a color that lies in the same quadrant as an unripe fruit as shown in Fig.…”

Section: Discussionmentioning

confidence: 89%

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Adainoo

Thomas

Krishnaswamy

2023

Sustainable Food Technol.

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“…When the value of DE* is 0, the two colors are identical, but when the value of DE* is 100, the two colors are least similar. A DE* value of 2.30 is indicated as just noticeable difference (JND) (Bhookya et al, 2020). Volatile profile of pears…”

Section: Color Measurementmentioning

confidence: 99%

“…The lightness value (L*) indicates the darkness/lightness of the sample (0 is total darkness and 100 is total lightness). The variable a* is a measurement of the greenness/redness of the sample (negative values are related to green color and positive ones the red color), and b* is a measurement of blueness/yellowness (negative values are related to blue color and positive ones the yellow color) (Bhookya et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Does bruising influence the volatile profile of pears?

Bodner

Scampicchio

2020

NFS

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Purpose Volatile flavor plays a main role in defining the fruit quality by consumers. Bruising leads often to dark spots on fruits and its amount could highly affect product quality. This paper aims to study the effect of bruising on the volatilome released by pears by using proton transfer reaction – mass spectrometry (PTR-MS). Design/methodology/approach Fingerprints of non-bruised and bruised pear samples were collected through PTR-MS for 28 days, and discriminant analysis was used to discriminate the fruit products. The CIELAB color changes were also measured during the entire ripening period. Findings Bruised pears released a higher intensity of volatile organic compounds (VOCs) compared to non-bruised pears (p16days = 0.049, p22days = 0.012, p28days = 0.006). In particular, the release of m/z 45 and m/z 47 were significantly (pm/z 45 = 0.076, pm/z 47 = 0.095.) higher in bruised samples, suggesting that the bruising event accelerated the natural ripening process. CIELAB color coordinates were also recorded. The coordinate a* showed a linear increase during the whole 28 days because of the loss of the green component. The CIELAB ΔE* was higher in the bruised pears than the non-bruised pears (p = 0.022). Originality/value Bruising can affect food quality and taste. Bruise susceptibility has been largely studied on apples, tomatoes and peaches, but rarely on pears. Very little is known about the effect of bruising on the volatilome of pears. Moreover, bruising research usually involved the study of physical properties; on the contrary, PTR-MS, applied to bruising research, has never been used before. Besides the analysis of volatilome, the changes in color were also recorded for the whole 28 days of analysis. The proposed method could be applied for the monitoring of pears quality in the food industry.

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Yield Estimation of Chilli Crop using Image Processing Techniques

Cited by 17 publications

References 10 publications

WITHDRAWN: Prediction of fruit quality parameters using peel color in Citrus Reticulata L. fruit by multiple linear regression and artificial neural network approach

WITHDRAWN: Prediction of fruit quality parameters using peel color in Citrus Reticulata L. fruit by multiple linear regression and artificial neural network approach

Correlations between color, textural properties and ripening of the North American pawpaw (Asimina triloba) fruit

Does bruising influence the volatile profile of pears?

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