Using AI in agriculture

Abstract

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.

Keywords

• Agriculture
• artificial intelligence
• smart agriculture
• precision agriculture

Tarımda AI kullanımı

Abstract

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.

Keywords

• Tarım
• yapay zeka
• akıllı tarım
• hassas tarım

References

1. Alexandratos, N. ve Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision.
2. 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.
3. Banu, S. (2015). Precision agriculture: tomorrow’s technology for today’s farmer. yayın yeri sayfa numaraları gibi bilgiler
4. Başaran, E. (2022). Image Wavelet Scattering and Densenet Based Pistachio Identification. Uluslararası Anadolu Ziraat Mühendisliği Bilimleri Dergisi, 4(3), 81–87.
5. 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.
6. 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.
7. 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.
8. Johnston, B.F. ve Kilby, P. (1975). Agriculture and structural transformation. Economic strategies in late-developing countries.

9. 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.
10. 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.
11. 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/
12. 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.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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
18. Sood, A., Sharma, R. K. ve Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: a review. Online Information Review, 46(6), 1054–1075.
19. 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
20. 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.
21. Wolfert, S., Ge, L., Verdouw, C. ve Bogaardt, M.-J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69–80.
22. Zhang, Q. (2016). Precision agriculture technology for crop farming. Taylor ve Francis.