The relationship between the appearance of pomegranate fruit and color and size of arils based on image processing

Fashi, Mahya; Naderloo, Leila; Javadikia, Hossein

doi:10.1016/j.postharvbio.2019.04.017

Cited by 34 publications

(17 citation statements)

References 12 publications

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“…This has the characteristic that emulates the functionality of human vision and allows spatial and optical information from the captured image of the sample. Different investigations have been reported with VC focused on determining the degrees of ripeness of various fruits such as persimmon, strawberries, pomegranate, and tomato [33][34][35]. The present work aims to implement an artificial vision system that automatically describes the ripeness levels of the bell pepper.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…The colour features have been already used to predict physiochemical properties of fruit. Recently, Fashi et al (2019) related the appearance of the pomegranate with the colours of the arils using image processing. Piedad et al (2019) used RGB coordinates with machine learning to classify banana tiers in different commercial qualities achieving 97 % of success.…”

Section: Introductionmentioning

confidence: 99%

Maturity monitoring of intact fruit and arils of pomegranate cv. ‘Mollar de Elche’ using machine vision and chemometrics

Munera

Hernández

Cubero

et al. 2019

Postharvest Biology and Technology

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Pomegranate fruit cv. 'Mollar de Elche' were collected at seven different harvest times. Colour and hyperspectral images of the intact fruit and arils were acquired at each harvest. Physicochemical properties such as total soluble solids, titratable acidity, maturity index, BrimA, internal colour, total phenolic compounds content and antioxidant activity were measured in the juice of each fruit. Relationships between colour (L*, a*, b*) and spectral (720-1050 nm) data obtained from the images of the intact fruit and arils were investigated physicochemical properties using partial least

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“…p cn 3 units organized in the form of the 2D array. The output is computed as per the unit at the position (m, n)and is evaluated as per Equations (23) and (24). i .…”

Section: Y Cnmentioning

confidence: 99%

“…In 2019, Fashi et al [24] have introduced a model to grade the pomegranate fruit on the basis of the size and colour of arils by deploying artificial intelligence and image processing. Three artificial intelligence algorithms have been [25] have reported about the growth of automatic adjustable segmentation algorithm of colour images by means of linear SVM and Otsu's thresholding model for apple grading and sorting.…”

Section: Related Workmentioning

confidence: 99%

Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

Tripathi

Maktedar

2021

IET Image Processing

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This paper intends to present an automated mango grading system under four stages (1) pre-processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre-processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre-processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA-FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine-tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For finetuning the convolutional layers in the deep CNN, the LA-FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state-of-the-art models. 1 INTRODUCTION Mango (Mangifera indica L.) belongs to the family Anacardiaceae. These are cultivated commercially and extensively in India, tropical Australia, Thailand, Philippines, Hawaii, the lowlands of SouthEast Africa, and in the lowlands of South and Central America. When exporting the mangoes over other countries, the grading [1-4] is essential for quality consideration. Conventionally, the fruit grading is handled by those trained inspectors and this is considered to be labour-intensive, time-consuming, and inefficient. The majority of the countries consider the size feature for mango grading. Still, it remains to be a complex task due to inappropriate grading. Therefore, the automatic grading process [5-7] is very necessary and helpful. On grading the mangoes, the features such as shape, size, firmness, maturity, and visual defects have to be essentially considered. Due to the advancement of technologies, the grading can be effectively made using image processing and computer vision systems [3-8]. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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The relationship between the appearance of pomegranate fruit and color and size of arils based on image processing

Cited by 34 publications

References 12 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

Maturity monitoring of intact fruit and arils of pomegranate cv. ‘Mollar de Elche’ using machine vision and chemometrics

Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

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