A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

Zareiforoush, Hemad; Minaei, Saeid; Alizadeh, Mohammad; Banakar, Ahmad

doi:10.1016/j.measurement.2015.01.022

Cited by 63 publications

(32 citation statements)

References 18 publications

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“…Other studies are developed fuzzy based decision support system for qualitative grading of milled rice [9]. Fuzzy decision support system was demonstrated to irrigation system [10] [11].…”

Section: Related Workmentioning

confidence: 99%

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

Hadiwiyanti¹,

Kusumantara²,

Anugrah³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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Quality measurement of rice is an important and necessary activity before it is sold to market. By distinguishing its quality, pricing will also be suitable by the physical characteristics of rice delivered by farmers. This paper proposed a prototype of DSS application to assist intermediaries in determining the price of rice according to its quality. Fuzzy triangular numbers and AHP method is used to evaluate and assess the rank of rice quality. The evaluation criterion are whiteness, grain shape, foreign materials, moisture content and damaged grains. Pricing is based on the Indonesian domestic retail prices in 2017. Keywords-decision support system; rice quality, priceI.

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“…Other studies are developed fuzzy based decision support system for qualitative grading of milled rice [9]. Fuzzy decision support system was demonstrated to irrigation system [10] [11].…”

Section: Related Workmentioning

confidence: 99%

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

Hadiwiyanti¹,

Kusumantara²,

Anugrah³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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show abstract

“…Fuzzy logic plays a key role in approximate reasoning, and Fuzzy Logic Systems [18][19][20] are implemented to manage approximate knowledge in order to solve specific problems through the use of approximate reasoning. The Mamdani fuzzy rule-based model is the most well-known fuzzy logic system model; it has been successfully applied in many fields, such as automatic control [21], expert systems [22], mobile robots [23], and computer vision [24].…”

Section: Introductionmentioning

confidence: 99%

A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Cardone

Martino

2018

Geosciences

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Here, we present a new unsupervised method aimed at obtaining a partition of a complex urban system in homogenous urban areas, called urban contexts. Our model integrates spatial analysis processes and a fuzzy rule-based system applied to manage the knowledge of domain experts; it is implemented using a GIS platform. The area of study is initially partitioned in microzones, homogeneous portions of the urban system, which are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental, and socio-economic indicators needed to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary for determining the type of urban context. We implement the set of spatial analysis processes required to calculate the indicators for the microzones and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples, Italy); the reliability of the out model is measured by comparing the results with the ones obtained through a detailed analysis.

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“…Sabanci et al [21] used ANFIS for wheat grain classification with 99.46% of classification accuracy. Zareiforoush et al [22] coupled a fuzzy inference system (FIS) with image processing technique for a decision-support system for qualitative grading of milled rice. The results are reported with 89.8% agreement between the grading results obtained from the FIS system and those determined by the experts.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

Mandal

2018

ASI

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Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with >98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

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A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

Cited by 63 publications

References 18 publications

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

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