Size Properties of Mangoes using Image Analysis

Ganiron, Tomas Jr

doi:10.14257/ijbsbt.2014.6.2.03

Cited by 26 publications

(10 citation statements)

References 21 publications

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“…We have the pneumatic pressure of common air compressors: p = 6 bar = 6.1183kgf / cm 2 The response of mango fruit is F = 7.5 N = 0.75 kg Select cylinder diameter is:…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…The recursive feature removal technique combined with the vector-based support (SVM) classifier is used to identify the most relevant features of the original 27 selected features. Finally, optimal aggregation of the number of reduced features is obtained and used to classify mangoes into four different types according to maturity level; Tomas U. Ganiron Jr developed a size-based mango classification system using image analysis techniques [2]. This empirical study aims to develop an efficient algorithm to detect and classify mangoes.…”

Section: Introductionmentioning

confidence: 99%

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Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Thong¹,

Thinh²,

Cong³

2019

IJET

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Sorting and Classification of mango, there are different colors, weights, sizes, shapes and densities. Currently, classification based on the above features is being carried out mainly by manuals due to farmers' awareness of low accuracy, high costs, health effects and high costs, costly economically inferior. This study was conducted on three main commercial mango species of Vietnam to find out the method of classification of mango with the best quality and accuracy. World studies of mango classification according to color, size, volume and almost done in the laboratory but not yet applied in practice. The quality assessment of mango fruit has not been resolved. Application of image processing technology, computer vision combined with artificial intelligence in the problem of mango classification or poor quality. The goal of the study is to create a system that can classify mangoes in terms of color, volume, size, shape and fruit density. The classification system using image processing incorporates artificial intelligence including the use of CCD cameras, C language programming, computer vision and artificial neural networks. The system uses the captured mango image, processing the split layer to determine the mass, volume and defect on the mango fruit surface. Especially, determine the density of mangoes related to its maturity and sweetness and determine the percentage of mango defects to determine the quality of mangoes for export and domestic or recycled mangoes. This article is about the development of an automatic mango classification system to control and evaluate mango quality before packaging and exporting to the market. It is in the research, design and fabrication of mango classification model and the completion of an automatic mango classification system using machine vision combining artificial intelligence. Index Terms-The classification of mango, sorting of mangoes, image processing technology, artificial intelligence; computer vision, artificial neural networks.

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“…We have the pneumatic pressure of common air compressors: p = 6 bar = 6.1183kgf / cm 2 The response of mango fruit is F = 7.5 N = 0.75 kg Select cylinder diameter is:…”

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Thong¹,

Thinh²,

Cong³

2019

IJET

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“…This can be easily estimated by counting number of pixels inside the boundary [15], Length is defined as the distance between the pole and the tip of the [15] and width is defined as the maximum distance from a boundary pixel to another boundary pixel that is on the other side of the major axis, and the line between them, which is called mango's minor axis, is a straight line at an angle of 90° to the major axis [15].…”

Section: Physical Property Analysismentioning

confidence: 99%

“…However, the image obtaining from scanner is not clean. A salt-and-pepper noise reduction technique must be applied [15].…”

Section:  Surface Areamentioning

confidence: 99%

Volume Estimation of Mango

Dhameliya¹,

Kakadiya²,

Savant³

2016

IJCA

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This paper observes the possibility of volume estimate of mango using image processing technique that are used in commercial packaging industry for increase the quality of fruits. Image processing is used to provide automation and cost-effective service to packaging industry for volume estimation of mango. Sizes featured are extracted from digit profile image using many image processing technique. The volume of fruit is obtained using single or multiple camera setups. The sizes featured are used to estimated volume of mango and compare with actual volume of mango. That program can measure the size property of mango such as height, width and area from image but there is one problem is that the size feature of mango is not accurate. We have to done some changes to achieve high accuracy of size featured of mango.

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“…Neural networks are used to analyse the diseases of fruits. The efficiency of detecting diseases of fruits is high by using neural networks [13] [15]. In advance adaptive probabilistic neural network and Adaptive general regression neural network is used to improve the efficiency [14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Sulphur Content in Copra

Sagayaraj¹,

Ramya²,

Dhanaraj³

2018

IJIVP

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Agriculture is the largest economic sector in India. Coconut is one of the most demanded fruit amongst all. The dried coconut, copra is the main source of coconut oil. Naturally it contains 70% of moisture content and it is dried to about 7% for production of coconut oil. The sulphur is added as preservative which acts as anti-microbial agent for preventing bacteria, fungus etc. Sulphur is a toxic food preservative which restricts lung performance and leads to direct allergenic reactions. The survey of World Health Organisation says that 65% of asthmatic children are sensitive to sulphur and 75% of children exposed to sulphur exhibits changes in their behaviour. The sulphur fumigation over coconut affects human both externally and internally. Fumigation leads to cancer and environmental pollution. In order to prevent this devastating effect, copra is examined using image processing. The proposed idea is to identify the presence and percentage of sulphur region present in copra. The region of interest is segmented by method of superimposition thereby segmenting white layers in copra. The RGB colour features are extracted to differentiate the sulphur added copra from normal copra. The coconut is dried under 60℃ in a tray drier and shapes of copra decreases at regular interval of time are extracted using image processing. The decreasing percentage of shape features are measured to identify the sulphur added in the copra. The k-means clustering technique is used to discriminate the copra at different levels. The segmented patch area is measured to determine the percentage of sulphur present in copra. The percentage of sulphur over copra is divided into three levels (low sulphur added region, medium sulphur added region and high sulphur added region). The K-Nearest Neighbour classification is also used to classify the sulphur added copra at different levels. The proposed algorithm classifies the sulphur added copra at three different levels with 86% accuracy.

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Size Properties of Mangoes using Image Analysis

Abstract: Abstract

Cited by 26 publications

References 21 publications

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Volume Estimation of Mango

Analysis of Sulphur Content in Copra

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