Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms
This study was conducted in order to determine the effect of the mechanical properties such as maximum force at the skin rupture point, energy at the skin rupture point and the skin firmness on color maturity of tomato by supervised learning algorithms of data mining. In the present study, a total o...
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Hasan Eleroğlu
2015-02-01
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| Series: | Turkish Journal of Agriculture: Food Science and Technology |
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| Online Access: | http://www.agrifoodscience.com/index.php/TURJAF/article/view/261 |
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| author | Hande Küçükönder Kubilay Kazım Vursavuş Fatih Üçkardeş |
| author_facet | Hande Küçükönder Kubilay Kazım Vursavuş Fatih Üçkardeş |
| author_sort | Hande Küçükönder |
| collection | DOAJ |
| description | This study was conducted in order to determine the effect of the mechanical properties such as maximum force at the skin rupture point, energy at the skin rupture point and the skin firmness on color maturity of tomato by supervised learning algorithms of data mining. In the present study, a total of 88 tomato samples were used, and color measurements for each tomato in 4 different equatorial regions were performed and a total of 352 color measurement units were used. In the classification processes performed according to these mechanical properties, K-Star, Random Forest and Decision Tree (C4.5) algorithms of data mining were utilized, and in the comparison of comprising classification models, Root Mean Square Error (RMSE), Mean absolute error (MAE), Root relative squared error (RRSE) and Relative absolute error (RAE) values, which are some of the criteria of error variance, were considered to be low, while the classification accuracy rate was considered to be high. As a result of the comparison made, the classification model formed according to K-Star instance-based algorithm [MAE: 0.004, RMSE: 0.006, %RAE: 1.73, %RRSE: 1.70] has been found to be a better classifier compared to the others. With the classification made according to K-Star algorithm, the maximum force at the skin rupture point on the degree of maturity of tomato and the skin firmness were found to be green, light red, and their effects are non-significant during the color conversion periods, and found significant during other periods while the energy at the skin rupture point is only pink and has been to be significant during the color conversion stages and non-significant during other stages. |
| format | Article |
| id | doaj-art-b18f67f0fc634583b5f33da105912e71 |
| institution | Kabale University |
| issn | 2148-127X |
| language | English |
| publishDate | 2015-02-01 |
| publisher | Hasan Eleroğlu |
| record_format | Article |
| series | Turkish Journal of Agriculture: Food Science and Technology |
| spelling | doaj-art-b18f67f0fc634583b5f33da105912e712025-08-20T03:39:25ZengHasan EleroğluTurkish Journal of Agriculture: Food Science and Technology2148-127X2015-02-013530030610.24925/turjaf.v3i5.300-306.261134Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification AlgorithmsHande Küçükönder0Kubilay Kazım Vursavuş1Fatih Üçkardeş2Bartın Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü, 74100 Bartın,Çukurova Üniversitesi, Ziraat Fakültesi, Tarım Makinaları Bölümü, 01330 Balcalı-Sarıçam/AdanaAdıyaman Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi, 02040 AdıyamanThis study was conducted in order to determine the effect of the mechanical properties such as maximum force at the skin rupture point, energy at the skin rupture point and the skin firmness on color maturity of tomato by supervised learning algorithms of data mining. In the present study, a total of 88 tomato samples were used, and color measurements for each tomato in 4 different equatorial regions were performed and a total of 352 color measurement units were used. In the classification processes performed according to these mechanical properties, K-Star, Random Forest and Decision Tree (C4.5) algorithms of data mining were utilized, and in the comparison of comprising classification models, Root Mean Square Error (RMSE), Mean absolute error (MAE), Root relative squared error (RRSE) and Relative absolute error (RAE) values, which are some of the criteria of error variance, were considered to be low, while the classification accuracy rate was considered to be high. As a result of the comparison made, the classification model formed according to K-Star instance-based algorithm [MAE: 0.004, RMSE: 0.006, %RAE: 1.73, %RRSE: 1.70] has been found to be a better classifier compared to the others. With the classification made according to K-Star algorithm, the maximum force at the skin rupture point on the degree of maturity of tomato and the skin firmness were found to be green, light red, and their effects are non-significant during the color conversion periods, and found significant during other periods while the energy at the skin rupture point is only pink and has been to be significant during the color conversion stages and non-significant during other stages.http://www.agrifoodscience.com/index.php/TURJAF/article/view/261DomatesMekanik özellikRenk ölçümK-StarRandom ForestKarar Ağacı (C4.5) |
| spellingShingle | Hande Küçükönder Kubilay Kazım Vursavuş Fatih Üçkardeş Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms Turkish Journal of Agriculture: Food Science and Technology Domates Mekanik özellik Renk ölçüm K-Star Random Forest Karar Ağacı (C4.5) |
| title | Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms |
| title_full | Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms |
| title_fullStr | Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms |
| title_full_unstemmed | Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms |
| title_short | Determining The Effect of Some Mechanical Properties on Color Maturity of Tomato With K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms |
| title_sort | determining the effect of some mechanical properties on color maturity of tomato with k star random forest and decision tree c4 5 classification algorithms |
| topic | Domates Mekanik özellik Renk ölçüm K-Star Random Forest Karar Ağacı (C4.5) |
| url | http://www.agrifoodscience.com/index.php/TURJAF/article/view/261 |
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