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Tea Purchase Center and Factory Matching with Fuzzy Logic Approach in Tea Production

Year 2022, , 461 - 478, 15.06.2022
https://doi.org/10.31466/kfbd.1093994

Abstract

Although countries are focused on the production and export of advanced technologies, defense and other industries, such as petroleum natural gas and metal products, they must maintain both economically and at least self-sufficient agricultural production. If the case will be examined in terms of Turkey, Turkey has a large and suitable area for agricultural activities. Turkey is a country where agricultural activities are carried out widely and effectively. Tea is one of the agricultural products produced in Turkey. An important point to be considered during the transportation of wet tea is that wet tea is a plant that burns very quickly and becomes unusable. Within the scope of this study, two provinces (Artvin and Rize), which are permitted by the government for planting tea, were taken into consideration to match factory-tea purchase unit location by fuzzy c-means clustering method. The use of clustering approach reduced the size of the problem and provided an easier solution. As a result of the analyses, more tea purchasing centers were assigned to some factories. The study shows that in addition to agricultural production, a correct structuring is required for the processing of agricultural products, such as the distribution of production areas and the location of processing facilities.

References

  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Boston, ABD: Springer.
  • Bhatt, P., Sarangi, S., Pappula, S. (2018). Coarse clustering and classification of images with CNN features for participatory sensing in agriculture. The International Conference on Pattern Recognition Applications and Methods (pp.488-495). Portugal.
  • Çay İşletmeleri Genel Müdürlüğü (Çaykur). (2017). İstatistik bülten, Retrieved from http://caykur.gov.tr/CMS/Design/Sources/Dosya/Yayinlar/281.pdf.
  • Çay İşletmeleri Genel Müdürlüğü (Çaykur). (2019). Üniteler. Retrieved from http://caykur.gov.tr/Pages/Tanitim/FotoGaleri.aspx?ItemId=222
  • Dragulescu, A., Arendt, C. (2020). xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.5. https://CRAN.R-project.org/package=xlsx
  • Dunn, J. C. (1974). Fuzzy relative of the ISODATA process and its use in detecting compact well-seperated clusters. Journal of Cybernetcis, 3(3), 32–57.
  • Emel, G. G., Taşkın, Ç. (2002). Genetik algoritmalar ve uygulama alanları. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 129-152.
  • Food and Agriculture Organization of the United Nations (FAO). (2018). Document CCP:TE 18/CRS1, Retrieved from http://www.invest-data.com/eWebEditor/uploadfile/2020041616145273605009.pdf.
  • Food and Agriculture Organization of the United Nations Statistics (FAOSTAT). Retrieved from http://www.fao.org/faostat/en/
  • Hamilton-Miller, J. M. T. (2001). Anti-cariogenic properties of tea (Camellia sinensis). Journal of medical microbiology, 50(4), 299-302.
  • John, H. (1992). Holland genetic algorithms. Scientific american, 267(1), 44-50.
  • Kassambara A., Mundt, F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra
  • Majumdar, J., Naraseeyappa, S., Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data, 4(1), 1-15.
  • Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2021). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-9. https://CRAN.R-project.org/package=e1071
  • Mitchell, M., & Forrest, S. (1994). Genetic algorithms and artificial life. Artificial life, 1(3), 267-289.
  • Mwangi, M., & Kariuki, S. (2015). Factors determining adoption of new agricultural technology by smallholder farmers in developing countries. Journal of Economics and sustainable development, 6(5), 208-206.
  • Nicolopoulou-Stamati, P., Maipas, S., Kotampasi, C., Stamatis, P., & Hens, L. (2016). Chemical pesticides and human health: the urgent need for a new concept in agriculture. Frontiers in Public Health, 4, 148.
  • Peeters, A., Zude, M., Käthner, J., Ünlü, M., Kanber, R., Hetzroni, A., Ben-Gal, A. (2015). A multivariate spatial clustering method for partitioning tree-based orchard data into homogenous zones. In Precision agriculture'15 (pp. 384-396). Wageningen Academic Publishers.
  • R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Retrieved from https://www.R-project.org.
  • RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
  • Scrucca, L. (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. https://doi.org/10.18637/jss.v053.i04
  • Servadio, P., Verotti, M. (2018). Fuzzy clustering algorithm to identify the effects of some soil parameters on mechanical aspects of soil and wheat yield. Spanish Journal of Agricultural Research, 16(4), e0206.
  • Singh, G., Atwal, S.K. (2017). Classification and clustering in yield prediction based on soil properties. International Journal of Advanced Research in Computer Science, 8(7), 253-258.
  • Sun, Z. X., Liu, J., Qiu, Z. L., Zhao, S. P., & Zang, L. (2003). Study on the variation of cold resistance of tea plant in shandong province. Journal of Tea Science, 23(1), 61-65.
  • Tarımsal Ekonomi ve Politika Geliştirme Enstitüsü. (2022). Tarım ürünleri piyasaları: Çay. Retrieved from https://arastirma.tarimorman.gov.tr/tepge/Belgeler/PDF Tar%C4%B1m %C3%9Cr%C3%BCnleri Piyasalar%C4%B1/2022-Ocak Tar%C4%B1m %C3%9Cr%C3%BCnleri Rapor%C4%B1/%C3%87ay, Ocak-2022, Tar%C4%B1m %C3%9Cr%C3%BCnleri Piyasa Raporu--+.pdf
  • Tea & Herbal Infusions Europe (THIE), Tea growing countries. Retrieved from http://www.thie-online.eu/tea/tea-growing-countries/.
  • Thambipillai, T. P. (2015). Strategic clustering of foundation suppliers of Sri Lankan tea and their impact on the macro supply network (Master dissertation). Retrieved from http://dl.lib.uom.lk/handle/123/10632.
  • Tie, J., Chen, W., Sun, C., Mao, T., Xing, G. (2018). The application of agglomerative hierarchical spatial clustering algorithm in tea blending. Cluster Computing, 22(3), 6059-6068.
  • Türkiye İstatistik Kurumu (TÜİK). (2019). Retrieved from https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr.
  • Tozlu, B. Okumuş, Şimşek, Aydemir, (2015). On-line monitoring of theaflavins and thearubigins ratio in Turkish black tea using electronic nose. International Journal of Engineering, 7(05), 8269.
  • Wu, X., Zhu, J., Wu, B., Sun, J., Dai, C. (2018). Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering. Computers and Electronics in Agriculture, 147, 64-69.

Çay Üretiminde Bulanık Mantık Yaklaşımıyla Çay Alım Merkezi ve Fabrika Eşleştirmesi

Year 2022, , 461 - 478, 15.06.2022
https://doi.org/10.31466/kfbd.1093994

Abstract

Ülkeler her ne kadar gelişmiş teknoloji, savunma ve diğer endüstri, petrol doğalgaz ve maden ürünlerinin üretimi ve ihracatı üzerine yoğunlaşmış olsa da hem ekonomik açıdan hem de en az kendi kendine yetecek düzeyde tarımsal üretimi de devam ettirmek durumdadır. Türkiye açısından durum incelenecek olursa, ülkemiz tarım faaliyetleri için oldukça uygun ve geniş alanlara sahip, kuruluşundan bugüne kadar yaygın ve etkin şekilde tarım faaliyetlerinin yürütüldüğü bir ülkedir. Türkiye’de üretilen tarım ürünlerinden birisi çaydır. Bu çalışma kapsamında, hükümet tarafından çay ekimi için izin verilen 2 il için (Artvin ve Rize) tüm ilçe ve köyleri göz önünde bulundurularak bulanık kümeleme yöntemi ile uzaklığa dayalı fabrika ve yaş çay alım yeri eşlemesi yapılmıştır. Kümeleme yaklaşımının kullanılması problemin boyutunu küçülterek daha kolay çözüm üretilmesini sağlamıştır. Analizler sonucunda bazı fabrikalara daha fazla sayıda çay alım merkezi atanmıştır. Çalışma tarımsal üretimin yanı sıra tarım ürünlerinin işlenebilmesi için de gerek üretim alanlarının dağılımı, gerek işleme tesislerinin konumlanması gibi konularda doğru bir yapılanmaya gereksinim olduğunu göstermektedir.

References

  • Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Boston, ABD: Springer.
  • Bhatt, P., Sarangi, S., Pappula, S. (2018). Coarse clustering and classification of images with CNN features for participatory sensing in agriculture. The International Conference on Pattern Recognition Applications and Methods (pp.488-495). Portugal.
  • Çay İşletmeleri Genel Müdürlüğü (Çaykur). (2017). İstatistik bülten, Retrieved from http://caykur.gov.tr/CMS/Design/Sources/Dosya/Yayinlar/281.pdf.
  • Çay İşletmeleri Genel Müdürlüğü (Çaykur). (2019). Üniteler. Retrieved from http://caykur.gov.tr/Pages/Tanitim/FotoGaleri.aspx?ItemId=222
  • Dragulescu, A., Arendt, C. (2020). xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.5. https://CRAN.R-project.org/package=xlsx
  • Dunn, J. C. (1974). Fuzzy relative of the ISODATA process and its use in detecting compact well-seperated clusters. Journal of Cybernetcis, 3(3), 32–57.
  • Emel, G. G., Taşkın, Ç. (2002). Genetik algoritmalar ve uygulama alanları. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 129-152.
  • Food and Agriculture Organization of the United Nations (FAO). (2018). Document CCP:TE 18/CRS1, Retrieved from http://www.invest-data.com/eWebEditor/uploadfile/2020041616145273605009.pdf.
  • Food and Agriculture Organization of the United Nations Statistics (FAOSTAT). Retrieved from http://www.fao.org/faostat/en/
  • Hamilton-Miller, J. M. T. (2001). Anti-cariogenic properties of tea (Camellia sinensis). Journal of medical microbiology, 50(4), 299-302.
  • John, H. (1992). Holland genetic algorithms. Scientific american, 267(1), 44-50.
  • Kassambara A., Mundt, F. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7. https://CRAN.R-project.org/package=factoextra
  • Majumdar, J., Naraseeyappa, S., Ankalaki, S. (2017). Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data, 4(1), 1-15.
  • Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2021). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-9. https://CRAN.R-project.org/package=e1071
  • Mitchell, M., & Forrest, S. (1994). Genetic algorithms and artificial life. Artificial life, 1(3), 267-289.
  • Mwangi, M., & Kariuki, S. (2015). Factors determining adoption of new agricultural technology by smallholder farmers in developing countries. Journal of Economics and sustainable development, 6(5), 208-206.
  • Nicolopoulou-Stamati, P., Maipas, S., Kotampasi, C., Stamatis, P., & Hens, L. (2016). Chemical pesticides and human health: the urgent need for a new concept in agriculture. Frontiers in Public Health, 4, 148.
  • Peeters, A., Zude, M., Käthner, J., Ünlü, M., Kanber, R., Hetzroni, A., Ben-Gal, A. (2015). A multivariate spatial clustering method for partitioning tree-based orchard data into homogenous zones. In Precision agriculture'15 (pp. 384-396). Wageningen Academic Publishers.
  • R Core Team. (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Retrieved from https://www.R-project.org.
  • RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.
  • Scrucca, L. (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1-37. https://doi.org/10.18637/jss.v053.i04
  • Servadio, P., Verotti, M. (2018). Fuzzy clustering algorithm to identify the effects of some soil parameters on mechanical aspects of soil and wheat yield. Spanish Journal of Agricultural Research, 16(4), e0206.
  • Singh, G., Atwal, S.K. (2017). Classification and clustering in yield prediction based on soil properties. International Journal of Advanced Research in Computer Science, 8(7), 253-258.
  • Sun, Z. X., Liu, J., Qiu, Z. L., Zhao, S. P., & Zang, L. (2003). Study on the variation of cold resistance of tea plant in shandong province. Journal of Tea Science, 23(1), 61-65.
  • Tarımsal Ekonomi ve Politika Geliştirme Enstitüsü. (2022). Tarım ürünleri piyasaları: Çay. Retrieved from https://arastirma.tarimorman.gov.tr/tepge/Belgeler/PDF Tar%C4%B1m %C3%9Cr%C3%BCnleri Piyasalar%C4%B1/2022-Ocak Tar%C4%B1m %C3%9Cr%C3%BCnleri Rapor%C4%B1/%C3%87ay, Ocak-2022, Tar%C4%B1m %C3%9Cr%C3%BCnleri Piyasa Raporu--+.pdf
  • Tea & Herbal Infusions Europe (THIE), Tea growing countries. Retrieved from http://www.thie-online.eu/tea/tea-growing-countries/.
  • Thambipillai, T. P. (2015). Strategic clustering of foundation suppliers of Sri Lankan tea and their impact on the macro supply network (Master dissertation). Retrieved from http://dl.lib.uom.lk/handle/123/10632.
  • Tie, J., Chen, W., Sun, C., Mao, T., Xing, G. (2018). The application of agglomerative hierarchical spatial clustering algorithm in tea blending. Cluster Computing, 22(3), 6059-6068.
  • Türkiye İstatistik Kurumu (TÜİK). (2019). Retrieved from https://biruni.tuik.gov.tr/medas/?kn=92&locale=tr.
  • Tozlu, B. Okumuş, Şimşek, Aydemir, (2015). On-line monitoring of theaflavins and thearubigins ratio in Turkish black tea using electronic nose. International Journal of Engineering, 7(05), 8269.
  • Wu, X., Zhu, J., Wu, B., Sun, J., Dai, C. (2018). Discrimination of tea varieties using FTIR spectroscopy and allied Gustafson-Kessel clustering. Computers and Electronics in Agriculture, 147, 64-69.
There are 31 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Fatma Önay Koçoğlu 0000-0002-1096-9865

Publication Date June 15, 2022
Published in Issue Year 2022

Cite

APA Koçoğlu, F. Ö. (2022). Tea Purchase Center and Factory Matching with Fuzzy Logic Approach in Tea Production. Karadeniz Fen Bilimleri Dergisi, 12(1), 461-478. https://doi.org/10.31466/kfbd.1093994