Bell pepper ripeness classification based on support vector machine

Elhariri, Esraa; El-Bendary, Nashwa; Hussein, Ahmed Mohammed; Hassanien, Aboul Ella; Badr, Amr

doi:10.1109/icengtechnol.2014.7016802

Cited by 21 publications

(10 citation statements)

References 10 publications

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“…According to the results, the models with FL achieved a maximum precision of 88% in identifying the four stages of maturity corresponding to the shades of green, yellow, orange, and red. The models with ANN have 100% precision to identify samples of green and red color similar to the results reported by Elhariri et al [41]. Of the results obtained, Model 8 was the one that presented a correlation of R = 0.79543 between the green, yellow, orange, and red regions of interest and ° Brix.…”

Section: Discussionsupporting

confidence: 84%

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A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

et al. 2020

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Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuum L. species whose consumption is popular in world gastronomy due to its wide variety of colors (ranging green, yellow, orange, red, and purple), shapes, and sizes and the absence of spicy flavor. In addition, these fruits have a characteristic flavor and nutritional attributes that include ascorbic acid, polyphenols, and carotenoids. A quality criterion for the harvest of this fruit is maturity; this attribute is visually determined by the consumer when verifying the color of the fruit’s pericarp. The present work proposes an artificial vision system that automatically describes ripeness levels of the bell pepper and compares the Fuzzy logic (FL) and Neuronal Networks for the classification stage. In this investigation, maturity stages of bell peppers were referenced by measuring total soluble solids (TSS), ° Brix, using refractometry. The proposed method was integrated in four stages. The first one consists in the image acquisition of five views using the Raspberry Pi 5 Megapixel camera. The second one is the segmentation of acquired image samples, where background and noise are removed from each image. The third phase is the segmentation of the regions of interest (green, yellow, orange and red) using the connect components algorithm to select areas. The last phase is the classification, which outputs the maturity stage. The classificatory was designed using Matlab’s Fuzzy Logic Toolbox and Deep Learning Toolbox. Its implementation was carried out onto Raspberry Pi platform. It tested the maturity classifier models using neural networks (RBF-ANN) and fuzzy logic models (ANFIS) with an accuracy of 100% and 88%, respectively. Finally, it was constructed with a content of ° Brix prediction model with small improvements regarding the state of art.

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Section: Discussionsupporting

confidence: 84%

“…Elhariri et al [41] used an algorithm based on a support vector machine (SVM) for classification. This identified five maturity classes associated with green and red shades.…”

Section: Related Workmentioning

confidence: 99%

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

et al. 2020

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“…In the literature, Elhariri et al (2014) proposed an algorithm for grading bell pepper to five different maturity classes from green to full maturity stage (red). The classification algorithm was developed based on the SVM technique as traditional intelligent modeling.…”

Section: Evaluation Of the Proposed Dcnn Classifier In The In-line Phasementioning

confidence: 99%

“…A total of 175 images, including different maturity classes were used to train and test the SVM algorithm. The accuracy of the bell pepper grading in off-line mode was 93.89% (Elhariri et al, 2014). In another study, Harel et al (2020) developed an image acquisition-based system coupled with two different classification algorithms for classifying bell peppers based on maturity.…”

Section: Evaluation Of the Proposed Dcnn Classifier In The In-line Phasementioning

confidence: 99%

“…To automate the sorting operation of bell peppers according to the export standards and to progress the packaging quality of this crop (by detecting and separating undesired bell peppers), developing an MV system based on computational intelligence techniques would be very important and effective. Previous studies have focused on the shallow learning algorithms in classifying bell peppers (Elhariri et al, 2014;Harel et al, 2020;Taofik et al, 2018). Such algorithms showed some drawbacks comprising sensitivity to optical noise, inability to differentiate on class boundaries, and so on.…”

Section: Introductionmentioning

confidence: 99%

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Design and evaluation of an intelligent sorting system for bell pepper using deep convolutional neural networks

Mohi-Alden

Omid

Firouz

et al. 2021

Journal of Food Science

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Homogeneity of appearance attributes of bell peppers is essential for consumers and food industries. This research aimed to develop an in-line sorting system using a deep convolutional neural network (DCNN) which is considered the state-of-the-art in the field of machine vision-based classifications, for grading bell peppers into five classes. According to export standards, the crop should be graded based on maturity stage and size. For that, the fully connected layer in the ResNet50 architecture of DCNN was replaced with a developed classifier block, including a global average-pooling layer, dense layers, batch normalization, and dropout layer. The developed model was trained and evaluated through the five-fold cross-validation method. The required processing time to classify each sample in the proposed model was estimated as 4 ms which is fast enough for real-time applications. Accordingly, the DCNN model was integrated with a machine vision-based designed sorting machine. Then, the developed system was evaluated in the in-line phase. The performance parameters in the in-line phase include accuracy, precision, sensitivity, specificity, F1-score, and overall accuracies were 98.7%, 97%, 96.9%, 99%, 96.9%, and 96.9%, respectively. The total rate of sorting the bell pepper was also measured as approximately 3000 sample/h with one sorting line. The proposed sorting system demonstrates a very good capability that allows it to be used in industrial applications.

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Selection and Fusion of Color Channels for Ripeness Classification of Cape Gooseberry Fruits

De-la-Torre

Ávila-George

Oblitas

et al. 2019

Advances in Intelligent Systems and Computing

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Bell pepper ripeness classification based on support vector machine

Cited by 21 publications

References 10 publications

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

Design and evaluation of an intelligent sorting system for bell pepper using deep convolutional neural networks

Selection and Fusion of Color Channels for Ripeness Classification of Cape Gooseberry Fruits

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