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Tarımda AI kullanımı

Yıl 2024, Cilt: 6 Sayı: 2, 145 - 152, 31.12.2024

Öz

Yaşamın devamlılığı için gerekli olan tarım sektörü dünya genelinde artan nüfus ve artan gıda ihtiyacı, su kısıtlılığı ve küresel ısınma gibi sorunlardan dolayı ciddi sıkıntılar yaşamaktadır. Yapay zeka, tarımda sulama ve ilaçlama sistemleri, toprak ve bitki analizi, hava durumu tahmini, ürün verimi, hastalık tespiti ve robot kullanımı gibi konularda kullanılmaktadır Bu amaçla yapay zeka kullanımı tarımda verimliliğin arttırılabilmesi ve sürdürülebilmesinin sağlanmasında çok önemli bir role sahiptir. Bu çalışmada, bahsedilen bu alanlardaki yapay zeka teknikleri incelenmiştir. İncelenen çalışmalarda tarım sektöründe yapay zeka sistemlerinin geleneksel yöntemlere kıyasla daha başarılı olduğu görülmüştür.

Destekleyen Kurum

Kahramanmaraş Sütçü İmam Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Kaynakça

  • Alexandratos, N. ve Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision.
  • Awokuse, T. O. ve Xie, R. (2015). Does agriculture really matter for economic growth in developing countries? Canadian Journal of Agricultural Economics/Revue Canadienne d’agroeconomie, 63(1), 77–99.
  • Banu, S. (2015). Precision agriculture: tomorrow’s technology for today’s farmer. yayın yeri sayfa numaraları gibi bilgiler
  • Başaran, E. (2022). Image Wavelet Scattering and Densenet Based Pistachio Identification. Uluslararası Anadolu Ziraat Mühendisliği Bilimleri Dergisi, 4(3), 81–87.
  • Boopalamani, J., Ayswariya, P. S. P., Raj, S. P., Yagnitha, P., Sarrvesh, N. ve Jha, A. (2024). A Survey of Drones in Agriculture Sector. Applied Mechanics and Materials, 919, 191–200.
  • Bronson, K. ve Knezevic, I. (2016). Big Data in food and agriculture. Big Data ve Society, 3(1), 2053951716648174. El Jaouhari, A. ve Hamidi, L. S. (2024). Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency. Technological Forecasting and Social Change, 200, 123149.
  • Gómez-Chabla, R., Real-Avilés, K., Morán, C., Grijalva, P. ve Recalde, T. (2019). IoT Applications in Agriculture: A Systematic Literature Review. Advances in Intelligent Systems and Computing, 901, 68–76.
  • Johnston, B.F. ve Kilby, P. (1975). Agriculture and structural transformation. Economic strategies in late-developing countries.
  • Karadeniz, A. T., Çelik, Y. ve Başaran, E. (2022). Classification of walnut varieties obtained from walnut leaf images by the recommended residual block based CNN model. European Food Research and Technology, 1–12.
  • Loja, L., Nedeff, V. ve Agop., M. (2024). Software uses in precision agriculture based on drone image processing – A review. International Conference on Energy Efficiency and Agricultural Engineering.
  • Özalp., A. (2022). Sürdürülebilir Tedarik Zincirinde Performans Göstergeleri. Erişim Tarihi: 17.10.2024. https://www.linkedin.com/pulse/s%C3%BCrd%C3%BCr%C3%BClebilir-tedarik-zincirinde-performans-an%C4%B1l-%C3%B6zalp/
  • Patel, K. G. ve Patil, M. S. (2022). Artificial intelligence in agriculture. International Journal for Research in Applied Science and Engineering Technology, 10(2), 624–627.
  • Patrício, D. I. ve Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69–81.
  • Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F. ve de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21–32.
  • Sachdeva, G., Singh, P. ve Kaur, P. (2021). Plant leaf disease classification using deep Convolutional neural network with Bayesian learning. Materials Today: Proceedings, 45, 5584–5590.
  • Sane, T. U. ve Sane, T. U. (2021). Artificial intelligence and deep learning applications in crop harvesting robots-A survey. 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 1–6.
  • Song, C., Ma, W., Li, J., Qi, B. ve Liu, B. (2022). Development Trends in Precision Agriculture and Its Management in China Based on Data Visualization. Agronomy, 12(11). https://doi.org/10.3390/agronomy12112905
  • Sood, A., Sharma, R. K. ve Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: a review. Online Information Review, 46(6), 1054–1075.
  • Tarım ve Orman Dergisi. (2023). Yapay zekâ tarımda kullanılabilir mi. Erişim Tarihi:17.10.2024. http://www.turktarim.gov.tr/Haber/958/yapay-zeka-tarimda-kullanilabilir-mi
  • Taşkıner, T. ve Bilgen, B. (2021). Optimization models for harvest and production planning in agri-food supply chain: A systematic review. Logistics, 5(3), 52.
  • Wolfert, S., Ge, L., Verdouw, C. ve Bogaardt, M.-J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69–80.
  • Zhang, Q. (2016). Precision agriculture technology for crop farming. Taylor ve Francis.

Using AI in agriculture

Yıl 2024, Cilt: 6 Sayı: 2, 145 - 152, 31.12.2024

Öz

The agricultural sector, which is necessary for the continuity of life, has experienced serious difficulties due to problems such as the increasing population and increasing food demand, water shortages and global warming in worldwide. The use of artificial intelligence has a very important role in increasing and sustaining productivity in agriculture. Artificial intelligence is used in agriculture in areas such as irrigation and spraying systems, soil and plant analysis, weather forecasting, product yield, disease detection and robot use. In this study, artificial intelligence techniques in these mentioned areas were examined. In the studies examined, it was seen that artificial intelligence systems in the agricultural sector were more successful than traditional methods.

Kaynakça

  • Alexandratos, N. ve Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision.
  • Awokuse, T. O. ve Xie, R. (2015). Does agriculture really matter for economic growth in developing countries? Canadian Journal of Agricultural Economics/Revue Canadienne d’agroeconomie, 63(1), 77–99.
  • Banu, S. (2015). Precision agriculture: tomorrow’s technology for today’s farmer. yayın yeri sayfa numaraları gibi bilgiler
  • Başaran, E. (2022). Image Wavelet Scattering and Densenet Based Pistachio Identification. Uluslararası Anadolu Ziraat Mühendisliği Bilimleri Dergisi, 4(3), 81–87.
  • Boopalamani, J., Ayswariya, P. S. P., Raj, S. P., Yagnitha, P., Sarrvesh, N. ve Jha, A. (2024). A Survey of Drones in Agriculture Sector. Applied Mechanics and Materials, 919, 191–200.
  • Bronson, K. ve Knezevic, I. (2016). Big Data in food and agriculture. Big Data ve Society, 3(1), 2053951716648174. El Jaouhari, A. ve Hamidi, L. S. (2024). Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency. Technological Forecasting and Social Change, 200, 123149.
  • Gómez-Chabla, R., Real-Avilés, K., Morán, C., Grijalva, P. ve Recalde, T. (2019). IoT Applications in Agriculture: A Systematic Literature Review. Advances in Intelligent Systems and Computing, 901, 68–76.
  • Johnston, B.F. ve Kilby, P. (1975). Agriculture and structural transformation. Economic strategies in late-developing countries.
  • Karadeniz, A. T., Çelik, Y. ve Başaran, E. (2022). Classification of walnut varieties obtained from walnut leaf images by the recommended residual block based CNN model. European Food Research and Technology, 1–12.
  • Loja, L., Nedeff, V. ve Agop., M. (2024). Software uses in precision agriculture based on drone image processing – A review. International Conference on Energy Efficiency and Agricultural Engineering.
  • Özalp., A. (2022). Sürdürülebilir Tedarik Zincirinde Performans Göstergeleri. Erişim Tarihi: 17.10.2024. https://www.linkedin.com/pulse/s%C3%BCrd%C3%BCr%C3%BClebilir-tedarik-zincirinde-performans-an%C4%B1l-%C3%B6zalp/
  • Patel, K. G. ve Patil, M. S. (2022). Artificial intelligence in agriculture. International Journal for Research in Applied Science and Engineering Technology, 10(2), 624–627.
  • Patrício, D. I. ve Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69–81.
  • Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F. ve de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21–32.
  • Sachdeva, G., Singh, P. ve Kaur, P. (2021). Plant leaf disease classification using deep Convolutional neural network with Bayesian learning. Materials Today: Proceedings, 45, 5584–5590.
  • Sane, T. U. ve Sane, T. U. (2021). Artificial intelligence and deep learning applications in crop harvesting robots-A survey. 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 1–6.
  • Song, C., Ma, W., Li, J., Qi, B. ve Liu, B. (2022). Development Trends in Precision Agriculture and Its Management in China Based on Data Visualization. Agronomy, 12(11). https://doi.org/10.3390/agronomy12112905
  • Sood, A., Sharma, R. K. ve Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: a review. Online Information Review, 46(6), 1054–1075.
  • Tarım ve Orman Dergisi. (2023). Yapay zekâ tarımda kullanılabilir mi. Erişim Tarihi:17.10.2024. http://www.turktarim.gov.tr/Haber/958/yapay-zeka-tarimda-kullanilabilir-mi
  • Taşkıner, T. ve Bilgen, B. (2021). Optimization models for harvest and production planning in agri-food supply chain: A systematic review. Logistics, 5(3), 52.
  • Wolfert, S., Ge, L., Verdouw, C. ve Bogaardt, M.-J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69–80.
  • Zhang, Q. (2016). Precision agriculture technology for crop farming. Taylor ve Francis.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Hassas Tarım Teknolojileri, Tarımsal Otomasyon
Bölüm Derlemeler
Yazarlar

Alper Talha Karadeniz 0000-0003-4165-3932

Erken Görünüm Tarihi 30 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 4 Ekim 2024
Kabul Tarihi 18 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 2

Kaynak Göster

APA Karadeniz, A. T. (2024). Tarımda AI kullanımı. AgriTR Science, 6(2), 145-152.