Classification of Date Fruits into Genetic Varieties Using Image Analysis

A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determin...

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Autores principales:	Murat Koklu, Ramazan Kursun, Yavuz Selim Taspinar, Ilkay Cinar
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f83a0b071ee34bd9b42d0fcdaba72e6b
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spelling	oai:doaj.org-article:f83a0b071ee34bd9b42d0fcdaba72e6b2021-11-22T01:10:50ZClassification of Date Fruits into Genetic Varieties Using Image Analysis1563-514710.1155/2021/4793293https://doaj.org/article/f83a0b071ee34bd9b42d0fcdaba72e6b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4793293https://doaj.org/toc/1563-5147A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.Murat KokluRamazan KursunYavuz Selim TaspinarIlkay CinarHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Murat Koklu Ramazan Kursun Yavuz Selim Taspinar Ilkay Cinar Classification of Date Fruits into Genetic Varieties Using Image Analysis
description	A great number of fruits are grown around the world, each of which has various types. The factors that determine the type of fruit are the external appearance features such as color, length, diameter, and shape. The external appearance of the fruits is a major determinant of the fruit type. Determining the variety of fruits by looking at their external appearance may necessitate expertise, which is time-consuming and requires great effort. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using three different machine learning methods. In accordance with this purpose, 898 images of seven different date fruit types were obtained via the computer vision system (CVS). Through image processing techniques, a total of 34 features, including morphological features, shape, and color, were extracted from these images. First, models were developed by using the logistic regression (LR) and artificial neural network (ANN) methods, which are among the machine learning methods. Performance results achieved with these methods are 91.0% and 92.2%, respectively. Then, with the stacking model created by combining these models, the performance result was increased to 92.8%. It has been concluded that machine learning methods can be applied successfully for the classification of date fruit types.
format	article
author	Murat Koklu Ramazan Kursun Yavuz Selim Taspinar Ilkay Cinar
author_facet	Murat Koklu Ramazan Kursun Yavuz Selim Taspinar Ilkay Cinar
author_sort	Murat Koklu
title	Classification of Date Fruits into Genetic Varieties Using Image Analysis
title_short	Classification of Date Fruits into Genetic Varieties Using Image Analysis
title_full	Classification of Date Fruits into Genetic Varieties Using Image Analysis
title_fullStr	Classification of Date Fruits into Genetic Varieties Using Image Analysis
title_full_unstemmed	Classification of Date Fruits into Genetic Varieties Using Image Analysis
title_sort	classification of date fruits into genetic varieties using image analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f83a0b071ee34bd9b42d0fcdaba72e6b
work_keys_str_mv	AT muratkoklu classificationofdatefruitsintogeneticvarietiesusingimageanalysis AT ramazankursun classificationofdatefruitsintogeneticvarietiesusingimageanalysis AT yavuzselimtaspinar classificationofdatefruitsintogeneticvarietiesusingimageanalysis AT ilkaycinar classificationofdatefruitsintogeneticvarietiesusingimageanalysis
_version_	1718418378918461440

Classification of Date Fruits into Genetic Varieties Using Image Analysis

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