The estimation of mango fruit total soluble solids using image processing technique

Shamili, Mansoore

doi:10.1016/j.scienta.2019.02.013

Cited by 16 publications

(10 citation statements)

References 27 publications

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Section: Discussionsupporting

confidence: 93%

“…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. This result is similar to that reported by Shamili [42] and is slightly superior to those reported by Leiva-Valenzuela et al [46] (R = 0.788) and Rahman et al [47] (R = 0.74). The advantage is that our proposal uses a visible RGB camera and they used a high-cost multispectral camera.…”

Section: Discussionsupporting

confidence: 91%

“…Shamili et al [42] proposed a polynomial regression model that estimated the soluble solids content of mango. The descriptor that he proposed was the normal values of * a of the images captured from five types of mangoes; the samples he used had different percentages of skin coloration of 0%, 20%, 25%, 50%, and more than 50%.…”

Section: Related Workmentioning

confidence: 99%

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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: 93%

Section: Discussionsupporting

confidence: 91%

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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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“…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][4][5][6][7][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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confidence: 99%

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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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Comparison of near-infrared spectroscopy and hyperspectral imaging for internal quality determination of ‘Nam Dok Mai’ mango during ripening

et al. 2022

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The estimation of mango fruit total soluble solids using image processing technique

Cited by 16 publications

References 27 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

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

Comparison of near-infrared spectroscopy and hyperspectral imaging for internal quality determination of ‘Nam Dok Mai’ mango during ripening

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