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The impact of sectors on agriculture based on artificial intelligence data: a case study on G7 countries and Turkiye

Yıl 2024, Cilt: 8 Sayı: 3, 486 - 494, 29.09.2024
https://doi.org/10.31015/jaefs.2024.3.1

Öz

The growing development of technology has had an impact on many sectors particularly business, communication, education and agriculture. In addition to its popularity, technology has brought many new concepts to the use of sectors, most of the important of which are cloud computing, artificial intelligence and cryptocurrencies. While the opportunities and concepts provided by technology have destroyed the existing job opportunities, they also introduced many positive opportunities like artificial intelligence, which can be considered as one of such positive innovations. The OECD artificial intelligence data of G7 countries and Turkey were used within the scope of this study. This study analyses the investment opportunities in agriculture and other sectors based on the artificial intelligence data. In addition to this study, both country-based and sectoral comparisons were made respectively. As a result, AI investments in the agricultural sector are generally at a lower level than other sectors. According to the analysis results, countries such as Türkiye and Canada are the countries that invest the most in the agricultural sector. This may reflect these countries' interest in agricultural potential and agricultural technology.

Kaynakça

  • Akour, M., & Alenezi, M. (2022). Higher education future in the era of digital transformation. Education Sciences, 12(11), 784.
  • Al Bashar, M., Taher, M. A., Islam, M. K., & Ahmed, H. (2024). The Impact Of Advanced Robotics And Automation On Supply Chain Efficiency In Industrial Manufacturing: A Comparative Analysis Between The Us And Bangladesh. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(03), 28-41.
  • Alaimo, C., Kallinikos, J., & Valderrama, E. (2020). Platforms as service ecosystems: Lessons from social media. Journal of information technology, 35(1), 25-48.
  • Anna, S. (2022). Implementing the OECD AI Principles: Challenges and Best Practices.
  • Atieh, A. T. (2021). The next generation cloud technologies: a review on distributed cloud, fog and edge computing and their opportunities and challenges. ResearchBerg Review of Science and Technology, 1(1), 1-15.
  • Atlı, H. F. (2023). Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. International Journal of Agriculture Environment and Food Sciences, 8(1), 25-43.
  • Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745.
  • Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 48(1), 123-134.
  • Canton, H. (2021). Organisation for economic co-operation and development—OECD. In The Europa Directory of International Organizations 2021 (pp. 677-687). Routledge.
  • Ceglar, A., & Toreti, A. (2021). Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting. Npj Climate and Atmospheric Science, 4(1), 42.
  • Çağlar, E. (2024) Integrating Innovative Technologies into Postharvest Fruit Storage Systems. In Postharvest Physiology and Handling of Horticultural Crops (pp. 234-244). CRC Press.
  • Demir, Y. (2021). G7 Ülkelerinde Ticari Dışa Açıklık, Finansal Açıklık ve Ekonomik Büyüme İlişkisi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 24(1), 274-287. (in Turkish)
  • Dhanya, V. G., Subeesh, A., Kushwaha, N. L., Vishwakarma, D. K., Kumar, T. N., Ritika, G., & Singh, A. N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6, 211-229.
  • Durai, S. K. S., & Shamili, M. D. (2022). Smart farming using machine learning and deep learning techniques. Decision Analytics Journal, 3, 100041.
  • Ercan, S. (2022). Türkiye’de Yapay Zekâ Yatirimlari: Stratejik Yönetim Bağlaminda Bir Değerlendirme, 127-142.
  • George, A. S., & George, A. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
  • Güzel, B., & Okatan, E. (2022). Tarim Ve Yapay Zekâ. Yapay Zekânin Değiştirdiği Dinamikler, 199. (in Turkish)
  • Haktanır, E., Kahraman, C., Şeker, Ş., & Doğan, O. (2022). Future of digital transformation. In Intelligent systems in digital transformation: Theory and applications (pp. 611-638). Cham: Springer International Publishing.
  • Hervas-Oliver, J. L., Sempere-Ripoll, F., & Boronat-Moll, C. (2021). Technological innovation typologies and open innovation in SMEs: Beyond internal and external sources of knowledge. Technological Forecasting and Social Change, 162, 120338.
  • Hu, T., Chitnis, N., Monos, D., & Dinh, A. (2021). Next-generation sequencing technologies: An overview. Human Immunology, 82(11), 801-811.
  • Iyer, L. S. (2021). AI enabled applications towards intelligent transportation. Transportation Engineering, 5, 100083.
  • Kahya, E., & Özdüven, F. (2023). An example of lettuce (Lactuca Sativa) seedling selection using deep learning method for robotic seedling selection system. International Journal of Agriculture Environment and Food Sciences, 7(2), 349-356.
  • Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312-322.
  • Khan, F. N., Sana, A., & Arif, U. (2020). Information and communication technology (ICT) and environmental sustainability: a panel data analysis. Environmental Science and Pollution Research, 27, 36718-36731.
  • Koca, D. (2022). 2000-2020 Yılları Arasında G7 Ülkeleri ve Türkiye’nin İşgücü Piyasası Yapısının ve Aktif İşgücü Piyasası Politikalarının Karşılaştırmalı Analizi. In Journal of Social Policy Conferences (No. 83, pp. 101-140). Istanbul University. (in Turkish)
  • Ma, H., & Zhang, X. (2022). Construction of smart marketing model of agricultural products E-commerce in the era of big data. Mobile Information Systems, 2022, 1-10.
  • Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., & Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.
  • Mayer, R. E. (2020). Where is the learning in mobile technologies for learning?. Contemporary Educational Psychology, 60, 101824.
  • OECD.AI (2024), visualisations powered by JSI using data from Preqin, accessed on 21/4/2024, https://oecd.ai/en/data?selectedArea=investments-in-ai-and-data
  • Özaydin, G., & Çelik, Y. (2019). Tarım Sektöründe Arge ve İnovasyon. Tarım Ekonomisi Dergisi, 25(1), 1-13. (in Turkish)
  • Özbilge, E., Kırsal, Y., & Çaglar, E. (2020). Modelling and analysis of IoT technology using neural networks in agriculture environment. International Journal of Computers Communications & Control, 15(3).
  • Paul, M., Maglaras, L., Ferrag, M. A., & Almomani, I. (2023). Digitization of healthcare sector: A study on privacy and security concerns. ICT Express, 9(4), 571-588.
  • Pazarbasioglu, C., Mora, A. G., Uttamchandani, M., Natarajan, H., Feyen, E., & Saal, M. (2020). Digital financial services. World Bank, 54.
  • Purnomo, Y. J. (2023). Digital marketing strategy to increase sales conversion on e-commerce platforms. Journal of Contemporary Administration and Management (ADMAN), 1(2), 54-62.
  • Santos, V., Augusto, T., Vieira, J., Bacalhau, L., Sousa, B. M., & Pontes, D. (2023). E-commerce: issues, opportunities, challenges, and trends. Promoting organizational performance through 5G and agile marketing, 224-244.
  • Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119.
  • Suman, A. (2021). Role of renewable energy technologies in climate change adaptation and mitigation: A brief review from Nepal. Renewable and Sustainable Energy Reviews, 151, 111524.
  • Tricot, R. (2021). Venture capital investments in artificial intelligence: Analysing trends in VC in AI companies from 2012 through 2020.
  • Ullo, S. L., & Sinha, G. R. (2021). Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sensing, 13(13), 2585.
  • Van Veldhoven, Z., & Vanthienen, J. (2022). Digital transformation as an interaction-driven perspective between business, society, and technology. Electronic markets, 32(2), 629-644.
  • Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.
  • Yürükoğlu, B. (2021). Yönetişim ve Ekonomik Performans: Türkiye ve G7 Ülkeleri İçin Bir Değerlendirme. Journal of Applied And Theoretical Social Sciences, 3(3), 244-262. (in Turkish)
  • Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256.
Yıl 2024, Cilt: 8 Sayı: 3, 486 - 494, 29.09.2024
https://doi.org/10.31015/jaefs.2024.3.1

Öz

Kaynakça

  • Akour, M., & Alenezi, M. (2022). Higher education future in the era of digital transformation. Education Sciences, 12(11), 784.
  • Al Bashar, M., Taher, M. A., Islam, M. K., & Ahmed, H. (2024). The Impact Of Advanced Robotics And Automation On Supply Chain Efficiency In Industrial Manufacturing: A Comparative Analysis Between The Us And Bangladesh. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(03), 28-41.
  • Alaimo, C., Kallinikos, J., & Valderrama, E. (2020). Platforms as service ecosystems: Lessons from social media. Journal of information technology, 35(1), 25-48.
  • Anna, S. (2022). Implementing the OECD AI Principles: Challenges and Best Practices.
  • Atieh, A. T. (2021). The next generation cloud technologies: a review on distributed cloud, fog and edge computing and their opportunities and challenges. ResearchBerg Review of Science and Technology, 1(1), 1-15.
  • Atlı, H. F. (2023). Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. International Journal of Agriculture Environment and Food Sciences, 8(1), 25-43.
  • Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745.
  • Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 48(1), 123-134.
  • Canton, H. (2021). Organisation for economic co-operation and development—OECD. In The Europa Directory of International Organizations 2021 (pp. 677-687). Routledge.
  • Ceglar, A., & Toreti, A. (2021). Seasonal climate forecast can inform the European agricultural sector well in advance of harvesting. Npj Climate and Atmospheric Science, 4(1), 42.
  • Çağlar, E. (2024) Integrating Innovative Technologies into Postharvest Fruit Storage Systems. In Postharvest Physiology and Handling of Horticultural Crops (pp. 234-244). CRC Press.
  • Demir, Y. (2021). G7 Ülkelerinde Ticari Dışa Açıklık, Finansal Açıklık ve Ekonomik Büyüme İlişkisi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 24(1), 274-287. (in Turkish)
  • Dhanya, V. G., Subeesh, A., Kushwaha, N. L., Vishwakarma, D. K., Kumar, T. N., Ritika, G., & Singh, A. N. (2022). Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture, 6, 211-229.
  • Durai, S. K. S., & Shamili, M. D. (2022). Smart farming using machine learning and deep learning techniques. Decision Analytics Journal, 3, 100041.
  • Ercan, S. (2022). Türkiye’de Yapay Zekâ Yatirimlari: Stratejik Yönetim Bağlaminda Bir Değerlendirme, 127-142.
  • George, A. S., & George, A. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
  • Güzel, B., & Okatan, E. (2022). Tarim Ve Yapay Zekâ. Yapay Zekânin Değiştirdiği Dinamikler, 199. (in Turkish)
  • Haktanır, E., Kahraman, C., Şeker, Ş., & Doğan, O. (2022). Future of digital transformation. In Intelligent systems in digital transformation: Theory and applications (pp. 611-638). Cham: Springer International Publishing.
  • Hervas-Oliver, J. L., Sempere-Ripoll, F., & Boronat-Moll, C. (2021). Technological innovation typologies and open innovation in SMEs: Beyond internal and external sources of knowledge. Technological Forecasting and Social Change, 162, 120338.
  • Hu, T., Chitnis, N., Monos, D., & Dinh, A. (2021). Next-generation sequencing technologies: An overview. Human Immunology, 82(11), 801-811.
  • Iyer, L. S. (2021). AI enabled applications towards intelligent transportation. Transportation Engineering, 5, 100083.
  • Kahya, E., & Özdüven, F. (2023). An example of lettuce (Lactuca Sativa) seedling selection using deep learning method for robotic seedling selection system. International Journal of Agriculture Environment and Food Sciences, 7(2), 349-356.
  • Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312-322.
  • Khan, F. N., Sana, A., & Arif, U. (2020). Information and communication technology (ICT) and environmental sustainability: a panel data analysis. Environmental Science and Pollution Research, 27, 36718-36731.
  • Koca, D. (2022). 2000-2020 Yılları Arasında G7 Ülkeleri ve Türkiye’nin İşgücü Piyasası Yapısının ve Aktif İşgücü Piyasası Politikalarının Karşılaştırmalı Analizi. In Journal of Social Policy Conferences (No. 83, pp. 101-140). Istanbul University. (in Turkish)
  • Ma, H., & Zhang, X. (2022). Construction of smart marketing model of agricultural products E-commerce in the era of big data. Mobile Information Systems, 2022, 1-10.
  • Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., & Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.
  • Mayer, R. E. (2020). Where is the learning in mobile technologies for learning?. Contemporary Educational Psychology, 60, 101824.
  • OECD.AI (2024), visualisations powered by JSI using data from Preqin, accessed on 21/4/2024, https://oecd.ai/en/data?selectedArea=investments-in-ai-and-data
  • Özaydin, G., & Çelik, Y. (2019). Tarım Sektöründe Arge ve İnovasyon. Tarım Ekonomisi Dergisi, 25(1), 1-13. (in Turkish)
  • Özbilge, E., Kırsal, Y., & Çaglar, E. (2020). Modelling and analysis of IoT technology using neural networks in agriculture environment. International Journal of Computers Communications & Control, 15(3).
  • Paul, M., Maglaras, L., Ferrag, M. A., & Almomani, I. (2023). Digitization of healthcare sector: A study on privacy and security concerns. ICT Express, 9(4), 571-588.
  • Pazarbasioglu, C., Mora, A. G., Uttamchandani, M., Natarajan, H., Feyen, E., & Saal, M. (2020). Digital financial services. World Bank, 54.
  • Purnomo, Y. J. (2023). Digital marketing strategy to increase sales conversion on e-commerce platforms. Journal of Contemporary Administration and Management (ADMAN), 1(2), 54-62.
  • Santos, V., Augusto, T., Vieira, J., Bacalhau, L., Sousa, B. M., & Pontes, D. (2023). E-commerce: issues, opportunities, challenges, and trends. Promoting organizational performance through 5G and agile marketing, 224-244.
  • Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119.
  • Suman, A. (2021). Role of renewable energy technologies in climate change adaptation and mitigation: A brief review from Nepal. Renewable and Sustainable Energy Reviews, 151, 111524.
  • Tricot, R. (2021). Venture capital investments in artificial intelligence: Analysing trends in VC in AI companies from 2012 through 2020.
  • Ullo, S. L., & Sinha, G. R. (2021). Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sensing, 13(13), 2585.
  • Van Veldhoven, Z., & Vanthienen, J. (2022). Digital transformation as an interaction-driven perspective between business, society, and technology. Electronic markets, 32(2), 629-644.
  • Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80.
  • Yürükoğlu, B. (2021). Yönetişim ve Ekonomik Performans: Türkiye ve G7 Ülkeleri İçin Bir Değerlendirme. Journal of Applied And Theoretical Social Sciences, 3(3), 244-262. (in Turkish)
  • Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal Politika (Diğer)
Bölüm Makaleler
Yazarlar

Ersin Çağlar 0000-0002-2175-5141

Yayımlanma Tarihi 29 Eylül 2024
Gönderilme Tarihi 22 Nisan 2024
Kabul Tarihi 15 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 3

Kaynak Göster

APA Çağlar, E. (2024). The impact of sectors on agriculture based on artificial intelligence data: a case study on G7 countries and Turkiye. International Journal of Agriculture Environment and Food Sciences, 8(3), 486-494. https://doi.org/10.31015/jaefs.2024.3.1

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International Journal of Agriculture, Environment and Food Sciences dergisinin içeriği, Creative Commons Alıntı-GayriTicari (CC BY-NC) 4.0 Uluslararası Lisansı ile yayınlanmaktadır. Söz konusu telif, üçüncü tarafların içeriği uygun şekilde atıf vermek koşuluyla, ticari olmayan amaçlarla paylaşımına ve uyarlamasına izin vermektedir. Yazarlar, International Journal of Agriculture, Environment and Food Sciences dergisinde yayınlanmış çalışmalarının telif hakkını elinde tutar. 

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