Journals / Karadeniz Fen Bilimleri Dergisi / 2022 / Cilt: 12 - Sayı: 1

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

Çay Üretiminde Bulanık Mantık Yaklaşımıyla Çay Alım Merkezi ve Fabrika Eşleştirmesi

Pages
461–478
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:If the case will be examined in terms of Turkey, Turkey has a large and suitable area for agricultural activities.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bulanık kümeleme · çay üretimi · çay fabrikası

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.

Özet

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.

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Cite This Article

KOÇOĞLU, F. Ö. (2022). Tea Purchase Center and Factory Matching with Fuzzy Logic Approach in Tea Production. Karadeniz Fen Bilimleri Dergisi, Cilt: 12 - Sayı: 1, 461–478. https://ajindex.com/articles/570359

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References (12)

  1. 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).
  2. 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
  3. 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
  4. 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.
  5. 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/
  6. Hamilton-Miller, J. M. T. (2001). Anti-cariogenic properties of tea (Camellia sinensis). Journal of medical microbiology, 50(4), 299-302.
  7. John, H. (1992). Holland genetic algorithms. Scientific american, 267(1), 44-50.
  8. 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
  9. 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.
  10. 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
  11. Mitchell, M., & Forrest, S. (1994). Genetic algorithms and artificial life. Artificial life, 1(3), 267-289.
  12. Mwangi, M., & Kariuki, S. (2015). Factors determining adoption of new agricultural technology by smallholder farmers in developing co