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Hasarsız Çarpma Tekniği Kullanarak Elma Meyvesinin Kütle Tahmini

Year 2021, Volume: 17 Issue: 2, 64 - 73, 31.08.2021

Abstract

Bu çalışmada, hasarsız çarpma tekniğini kullanarak elma kütlesini tahmin etmek ve farklı model yaklaşımları geliştirmek amaçlanmıştır. Deneylerde Starkrimson elma çeşitleri kullanılmıştır. Elma kütlesinin tahmininde, 10 hasarsız çarpma parametresi, çarpma kuvveti-zaman eğrileri kullanılarak dikkate alınmış ve matematiksel modelde kullanılmak üzere stepwise regresyon analizi yöntemi ile çarpma parametrelerinin sayısı azaltılmıştır (Fmax1, tmax, tmax1, Ia and tP1-2). Elma kütle tahmini, bu parametreler kullanılarak çoklu doğrusal regresyon analizi yöntemiyle (MLR) yapılmıştır. İstatistiksel analiz sonuçlarına göre geliştirilen matematiksel model, elma kütlesini kalibrasyon ve doğrulama veri setinde sırasıyla 3.07 g ve 3.35 g tahmin hatası ile tahmin etmiştir. Kalibrasyon ve doğrulama veri setinde elma kütle tahmini belirleme katsayıları (R2) sırasıyla 0,94 ve 0,93 olarak hesaplanmıştır. Kümeleme analizine göre sınıflandırılan kütle gruplarına göre kütle tahmin modelinin başarısı da belirlenmiştir. Veri grubu analizi sonuçlarına göre model yaklaşımının gerçek doğruluğu 32 olarak, ayrıca elma örneklerinin sınıflandırma başarısı da %94,11 olarak hesaplanmıştır.

Supporting Institution

Çukurova Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

10362

References

  • Anonymous, 2017. Fresh Fruit and Vegetable Sector. Sector Reports. Republic of Turkey, Ministry of Economy, Export Directorate-General, Department of Agricultural Products, pp: 14.
  • Delwiche, M.J., MacDonald, T., Bowers, S.V., 1987, Determination of peach firmness by analysis of impact forces, T ASAE, 30, 249-254.
  • Elbeltagi, F., 2011. High Speed Weighing System Analysis via Mathematical Modelling. Master of Sccience Thesis. Massey University, Albay, Auckland, New Zealand, pp: 116.
  • Ghazavi, M.A., Karami, R., ve Mahmoodi, M. 2013. Modeling Some Physical-Mechanical Properties of Tomato. Journal of Agricultural Science, 5(1): 210-223.
  • Gutierrez, A., Burgos, J.A., Molto, E., 2007, Pre-commercial sorting line for peaches firmness assessment, J Food Eng, 81, 721-727.
  • Izadi, H., Kamgar, S., Raufat, M.H., ve Samsami, S. 2014. Mass and Volume Modeling of Tomato Based on Pphysical Characteristics. Scientific Journal of Crop Science, 3(1): 1-8.
  • Khoshnam, F., Tabatabaeefar, A., Ghasemi Varnamkhasti, ve M., Borghei, A. 2007. Mass Modeling of Pomegranate (Punica granatum L.) Fruit with Some Physical Characteristics. Scientia Horticulturae, 11(4): 21-26.
  • Lien, C.C., Ay, C., Tingh, C.H., 2009, Non-destructive impact test for assessment of tomato maturity, J Food Eng, 91, 402-407.
  • McGlone, V.A., Jordan, R.B., ve Schaare, P.N. 1997. Obtaining Mass from Fruit Impact Response. Transactions of the ASAE, 40(5): 1417-1419.
  • Qarallah, B., Shoji, K., ve Kawamura, T. 2008. Development of a Yield Sensor for Measuring Individual Weighs of Onion Bulbs. Biosystems Engineering, 100(4): 511-515.
  • Ragni, L., Berardinelli, A., 2010, Impact device for measuring the flesh firmness of kiwifruits, J Food Eng, 96, 591-597. Sabzi, S., Javadikia, P., Rabani, H., ve Adelkhani, A. 2013. Mass Modeling of Bam Orange with ANFIS and SPSS Methods for Using in Machine Vision. Measurement, 46: 3333-3341.
  • Schulze, K.S., Nagle, M., Spreer, W., Mayahothee, B., ve Müller, J. 2015. Development and Assessment of Different Modeling Approaches for Size-Mass Prediction of Mango Fruits (Mangifera indica L., cv. “Nam Dokmai”). Computers and Electronics in Agriculture, 14: 269-276.
  • Shahbazi, F., ve Rahmati, S. 2012. Mass modeling of Fig (Ficus carica L.) Fruit with Some Physical Characteristics. Food Science & Nutrition, 1(2): 125-129.
  • Spreer, W., ve Müller, J. 2011. Estimating the Mass of Mango Fruit (Mangifera indica, cv. Chok Anan) from its Geometric Dimensions by Optical Measurement. Computers and Electronics in Agriculture, 75: 125-131.
  • Stropek, Z., ve Golacki, K., 2007. Determining Apple Mass on the Basis of Rebound Energy During Impact. TEKA Kom. Mot. Energ. Roln., 7(A): 100-105.
  • Tabatabaeefar A., ve Rajabipour, A. 2005. Modeling the Mass of Apples by Geometrical Attributes. Scientia Horticulturae, 105: 373-382.
  • Tüdeş, E., 2019. Hasarsız Çarpma Tekniği Kullanarak Elma Meyvesinin Kütle Tahmini İçin Farklı Model Yaklaşımların Değerlendirilmesi. Ç.Ü. Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, 35 s.
  • Vursavuş, K.K., ve Ince, A., 2007. Influence of Apple Region and Variety on the Mechanical Properties and Bruise Threshold of Apples. Tarım Makinaları Bilimi Dergisi, 3(4): 257-266.
  • Vursavuş, K.K., ve Özgüven, F., 2008. Modeling the Mass of Oranges by Geometrical Properties. 10th International Congress on Mechanization and Energy in Agriculture. 14-17 October 2008, Antalya, pp: 745-751.
  • Vursavuş, K.K., ve Kesilmiş, Z., 2016. Hasarsız Çarpma Tekniği Kullanılarak Domates Meyvesinin Kütle Tahmini İçin Farklı Model Yaklaşımlarının Geliştirilmesi ve Değerlendirilmesi. Anadolu Tarım Bilimleri Dergisi, 31: 385-392.
  • Vursavuş, K.K., Kesilmiş, Z., Öztekin, Y.B., 2017. Nondestructive Dropped Fruit Impact Test for Assessing Tomato Firmness. Chemical Engineering Transactions, 58: 325-330.

Mass Prediction of Apple Fruit by Using Nondestructive Impact Technique

Year 2021, Volume: 17 Issue: 2, 64 - 73, 31.08.2021

Abstract

In this study, it was aimed to estimate the mass of apples by using the nondestructive impact technique and to develop different model approaches. Starkrimson apple varieties were used in the experiments. In the prediction of apple mass, 10 nondestructive impact parameters were taken into consideration using impact force-time curves, and the number of impact parameters were reduced by stepwise regression analysis method to be used in the mathematical model (Fmax1, tmax, tmax1, Ia and tP1-2). Apple mass prediction was made by using these parameters in the multiple linear regression analysis method (MLR). According to the results of statistical analysis, developed mathematical model predicted the apple mass with 3.07 g and 3.35 g prediction error in the calibration and validation data set, respectively. In the calibration and validation data set, determination coefficients of the apple mass prediction (R2) were calculated as 0.94 and 0.93, respectively. The success of the mass prediction model according to the mass groups classified according to the cluster analysis was also determined. According to the results of the data group analysis, the true accuracy of the model approach was calculated as 32. In addition, the success of the classification of apple samples was calculated as 94.11%.

Project Number

10362

References

  • Anonymous, 2017. Fresh Fruit and Vegetable Sector. Sector Reports. Republic of Turkey, Ministry of Economy, Export Directorate-General, Department of Agricultural Products, pp: 14.
  • Delwiche, M.J., MacDonald, T., Bowers, S.V., 1987, Determination of peach firmness by analysis of impact forces, T ASAE, 30, 249-254.
  • Elbeltagi, F., 2011. High Speed Weighing System Analysis via Mathematical Modelling. Master of Sccience Thesis. Massey University, Albay, Auckland, New Zealand, pp: 116.
  • Ghazavi, M.A., Karami, R., ve Mahmoodi, M. 2013. Modeling Some Physical-Mechanical Properties of Tomato. Journal of Agricultural Science, 5(1): 210-223.
  • Gutierrez, A., Burgos, J.A., Molto, E., 2007, Pre-commercial sorting line for peaches firmness assessment, J Food Eng, 81, 721-727.
  • Izadi, H., Kamgar, S., Raufat, M.H., ve Samsami, S. 2014. Mass and Volume Modeling of Tomato Based on Pphysical Characteristics. Scientific Journal of Crop Science, 3(1): 1-8.
  • Khoshnam, F., Tabatabaeefar, A., Ghasemi Varnamkhasti, ve M., Borghei, A. 2007. Mass Modeling of Pomegranate (Punica granatum L.) Fruit with Some Physical Characteristics. Scientia Horticulturae, 11(4): 21-26.
  • Lien, C.C., Ay, C., Tingh, C.H., 2009, Non-destructive impact test for assessment of tomato maturity, J Food Eng, 91, 402-407.
  • McGlone, V.A., Jordan, R.B., ve Schaare, P.N. 1997. Obtaining Mass from Fruit Impact Response. Transactions of the ASAE, 40(5): 1417-1419.
  • Qarallah, B., Shoji, K., ve Kawamura, T. 2008. Development of a Yield Sensor for Measuring Individual Weighs of Onion Bulbs. Biosystems Engineering, 100(4): 511-515.
  • Ragni, L., Berardinelli, A., 2010, Impact device for measuring the flesh firmness of kiwifruits, J Food Eng, 96, 591-597. Sabzi, S., Javadikia, P., Rabani, H., ve Adelkhani, A. 2013. Mass Modeling of Bam Orange with ANFIS and SPSS Methods for Using in Machine Vision. Measurement, 46: 3333-3341.
  • Schulze, K.S., Nagle, M., Spreer, W., Mayahothee, B., ve Müller, J. 2015. Development and Assessment of Different Modeling Approaches for Size-Mass Prediction of Mango Fruits (Mangifera indica L., cv. “Nam Dokmai”). Computers and Electronics in Agriculture, 14: 269-276.
  • Shahbazi, F., ve Rahmati, S. 2012. Mass modeling of Fig (Ficus carica L.) Fruit with Some Physical Characteristics. Food Science & Nutrition, 1(2): 125-129.
  • Spreer, W., ve Müller, J. 2011. Estimating the Mass of Mango Fruit (Mangifera indica, cv. Chok Anan) from its Geometric Dimensions by Optical Measurement. Computers and Electronics in Agriculture, 75: 125-131.
  • Stropek, Z., ve Golacki, K., 2007. Determining Apple Mass on the Basis of Rebound Energy During Impact. TEKA Kom. Mot. Energ. Roln., 7(A): 100-105.
  • Tabatabaeefar A., ve Rajabipour, A. 2005. Modeling the Mass of Apples by Geometrical Attributes. Scientia Horticulturae, 105: 373-382.
  • Tüdeş, E., 2019. Hasarsız Çarpma Tekniği Kullanarak Elma Meyvesinin Kütle Tahmini İçin Farklı Model Yaklaşımların Değerlendirilmesi. Ç.Ü. Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, 35 s.
  • Vursavuş, K.K., ve Ince, A., 2007. Influence of Apple Region and Variety on the Mechanical Properties and Bruise Threshold of Apples. Tarım Makinaları Bilimi Dergisi, 3(4): 257-266.
  • Vursavuş, K.K., ve Özgüven, F., 2008. Modeling the Mass of Oranges by Geometrical Properties. 10th International Congress on Mechanization and Energy in Agriculture. 14-17 October 2008, Antalya, pp: 745-751.
  • Vursavuş, K.K., ve Kesilmiş, Z., 2016. Hasarsız Çarpma Tekniği Kullanılarak Domates Meyvesinin Kütle Tahmini İçin Farklı Model Yaklaşımlarının Geliştirilmesi ve Değerlendirilmesi. Anadolu Tarım Bilimleri Dergisi, 31: 385-392.
  • Vursavuş, K.K., Kesilmiş, Z., Öztekin, Y.B., 2017. Nondestructive Dropped Fruit Impact Test for Assessing Tomato Firmness. Chemical Engineering Transactions, 58: 325-330.
There are 21 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ecenur Tüdeş This is me

Kubilay Vursavuş 0000-0001-8674-653X

Project Number 10362
Early Pub Date August 31, 2021
Publication Date August 31, 2021
Published in Issue Year 2021 Volume: 17 Issue: 2

Cite

APA Tüdeş, E., & Vursavuş, K. (2021). Mass Prediction of Apple Fruit by Using Nondestructive Impact Technique. Tarım Makinaları Bilimi Dergisi, 17(2), 64-73.

Journal of Agricultural Machinery Science is a refereed scientific journal published by the Agricultural Machinery Association as 3 issues a year.