The era of artificial ıntelligence in agriculture: ecosystemic and operational dimensions of a multilayered digital transformation

Abstract

The agricultural sector is recognized as a strategic field in terms of food production, employment, and economic contribution. However, factors such as climate change, land fragmentation, lack of knowledge, and population growth have adverse effects on the sector. Historically, productivity has been increased through technological advancements such as machinery and the Green Revolution. Currently, sustainability and precision are being enhanced through technologies including the Global Positioning System (GPS), Geographic Information Systems (GIS), the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data. Artificial intelligence plays a critical role in reducing environmental impacts by improving agricultural productivity, early disease detection, and input optimization. Nevertheless, the success of digital transformation depends not only on technological infrastructure but also on social and political dimensions such as digital literacy, data security, and ethical governance. Access to and adaptation of digital technologies by small-scale farmers is considered a significant barrier. Therefore, the development of accessibility, education, inclusive policies, and ethical frameworks is necessary. Digital agriculture is defined as a holistic transformation process encompassing social, economic, and political dimensions, rather than merely a technical shift. Successful digitalization is made possible through equitable and user-centered approaches alongside technological infrastructure. Accordingly, this comprehensive review explores the opportunities presented by technological advancements and digitalization in agriculture, identifies the key challenges faced, and discusses potential solutions.

Keywords

• Artificial Intelligence
• agriculture
• digital transformation in agriculture
• sustainability

Tarımda yapay zekâ çağı: çok katmanlı dijital dönüşümün ekosistemsel ve operasyonel boyutları

Öz

Tarım sektörü, besin üretimi, istihdam ve ekonomik katkı açısından stratejik bir alan olarak kabul edilmektedir. Ancak iklim değişikliği, arazi parçalanması, bilgi eksikliği ve nüfus artışı gibi etmenler sektör üzerinde olumsuz etkiler yaratmaktadır. Tarihsel süreçte makineler ve Yeşil Devrim gibi teknolojik gelişmelerle verimlilik artırılmıştır. Günümüzde ise küresel konum belirleme sistemi (Global Positioning System, GPS), coğrafi bilgi sistemi (Geographic Information System, GIS), nesnelerin interneti (Internet of Things, IoT), yapay zekâ (Artificial Intelligence, AI) ve büyük veri (Big Data) teknolojileri sayesinde sürdürülebilirlik ve hassasiyet artırılmaktadır. Yapay zekâ, tarımsal verimlilik, hastalık erken teşhisi ve girdi optimizasyonu yoluyla çevresel etkilerin azaltılmasında kritik bir rol üstlenmektedir. Ancak dijital dönüşümün başarısı, teknolojik altyapının yanı sıra dijital okuryazarlık, veri güvenliği ve etik yönetişim gibi sosyal ve politik boyutlara bağlıdır. Özellikle küçük ölçekli çiftçilerin dijital teknolojiye erişimi ve adaptasyonu önemli bir engel olarak görülmektedir. Bu nedenle, erişilebilirlik, eğitim, kapsayıcı politikalar ve etik çerçevelerin geliştirilmesi gerekmektedir. Dijital tarım, yalnızca teknik bir dönüşüm olmayıp sosyal, ekonomik ve politik boyutları içeren bütüncül bir değişim süreci olarak tanımlanmaktadır. Başarılı bir dijitalleşme süreci, teknolojik altyapının yanında adil ve kullanıcı merkezli yaklaşımlar ile mümkün kılınmaktadır. Bu bağlamda, tarımda teknoloji ve dijitalleşmenin sunduğu olanakları, karşılaşılan zorlukları ve çözüm önerilerini ele alan kapsamlı bir derleme çalışması gerçekleştirilmiştir.

Anahtar Kelimeler

• Yapay zeka
• tarım
• tarımda dijital dönüşüm
• sürdürülebilirlik

References

1. Abioye, A. E., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Mohammed, O. O., Otuoze, A. O., et al. (2023). Model based predictive control strategy for water saving drip irrigation. Smart. Agric. Technol. 4, 100179. doi: 10.1016/j.atech.2023.100179
2. Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762. https://doi.org/10.1016/j.jafr.2025.101762
3. Ait-Tchekki, A., & Durand, P. (2024). Machine learning for a better agriculture calendar. In Proceedings of the 11th International Conference on Smart Agriculture (pp. 145–154). SciTePress.
4. Albuquerque, J. R. D. P., Makara, C. N., Ferreira, V. G., Brazaca, L. C., & Carrilho, E. (2024). Low-cost precision agriculture for sustainable farming using paper-based analytical devices. RSC Advances, 14(32), 23392–23403. https://doi.org/10.1039/D4RA02310B
5. Andrews, J. (2024, June 12). Printed sensors in soil could help farmers improve crop yields and save money. University of Wisconsin–Madison News.
6. Araya, S., Liu, Y., & Johnson, R. (2024). Assessment of climate change impact on rain-fed corn yield with DSSAT across the Mississippi River Basin. Agricultural Systems, 224, 103599.
7. Aslan, M. F., Sabanci, K., & Aslan, B. (2024). Artificial intelligence techniques in crop yield estimation based on Sentinel-2 data: A comprehensive survey. Sustainability, 16(18), 8277. https://doi.org/10.3390/su16188277 Avary Drone. (2025, March 3). The economics of drone spraying: ROI for farmers in 2025.
8. Aydınbaş, G. (2023). İktisadi perspektiften akıllı tarım (Tarım 4.0) üzerine bir inceleme. BİLTÜRK Journal of Economics and Related Studies, 5(2), 63–86. https://doi.org/10.47103/bilturk.1218500

9. Balafoutis, A. T., Beck, B., Fountas, S., Tsiropoulos, Z., Vangeyte, J., van der Wal, T., et al. (2017). Smart Farming Technologies – Description, Taxonomy and Economic Impact. In: Pedersen, S., Lind, K. (eds) Precision Agriculture: Technology and Economic Perspectives. Progress in Precision Agriculture. Springer, Cham. doi: 10.1007/978-3-319-68715-5_2
10. Borgogno Mondino, E., Gajetti, M. (2017). Preliminary considerations about costs and potential market of remote sensing from UAV in the Italian viticulture context. Eur. J. Remote Sens. 50, 310–319. doi: 10.1080/22797254.2017.1328269
11. Bronson, K., & Knezevic, I. (2016). Big data in food and agriculture. Big Data & Society, 3(1), 2053951716648174. https://doi.org/10.1177/2053951716648174
12. Business Insider. (2025, May 1). Farmers are using IoT to take the guesswork out of growing. Business Insider Agriculture.
13. Chiu, M.-C., Yan, W.-M., Bhat, S. A., & Huang, N.-F. (2022). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research, 9, 100357. https://doi.org/10.1016/j.jafr.2022.100357
14. Choruma, D. J., Akamagwuna, F. C., & Odume, N. O. (2022). Simulating the impacts of climate change on maize yields using EPIC: A case study in the Eastern Cape Province of South Africa. Agriculture, 12(6), 794. https://doi.org/10.3390/agriculture12060794
15. Çilesiz, Y., & Karaköy, T. (2022). Akıllı tarım teknolojilerinin tarımsal üretimde kullanımı. In Teknolojik Tarım (pp. 3–26).
16. Crossa, J., Alemu, A., & Tadesse, W. (2024). Improving plant breeding through AI-supported data integration. Molecular Plant, 17(8), 1453–1467. https://doi.org/10.1016/j.molp.2024.03.007
17. Coughlan, P., Kingwell, R., & Hayman, P. (2024). Developing climate services for Australian agriculture: The My Climate View tool case study. Climate Services, 38, 100492.
18. Das, V. J., Sharma, S., Kaushik, A. (2019). Views of Irish farmers on smart farming technologies: An observational study. AgriEngineering 1, 164–187. doi: 10.3390/agriengineering1020013
19. Eastwood, C., Klerkx, L., & Nettle, R. (2017). Dynamics and distribution of public and private research and extension roles for technological innovation and diffusion: Case studies of the implementation and adaptation of precision farming technologies. Journal of Rural Studies, 49, 1–12.
20. El-Mahroug, S. E., Suleiman, A. A., & Zoubi, M. M. (2025). Predictive modeling of climate-driven crop yield variability using DSSAT towards sustainable agriculture. AgriEngineering, 7(5), 156. https://doi.org/10.3390/agriengineering7050156
21. Emanuel, N. (2025, March 17). The rise of variable rate & irrigation optimization. CropX. https://cropx.com/2025/03/17/variable-rate-irrigation/
22. Evans, D. (2011). The internet of things. How the Next Evolution of the Internet is Changing Everything, Whitepaper, Cisco Internet Business Solutions Group (IBSG), Vol. 1. 1–12.
23. Feng, X., Wang, Y., & Mir, Z. A. (2025). WheatGP: A CNN-LSTM genomic prediction framework for grain yield improvement. Frontiers in Plant Science, 16, 1324090. https://doi.org/10.3389/fpls.2024.1324090 Freeman, P. K., Freeland, R. S. (2015). Agricultural UAVs in the US: potential, policy, and hype. Remote Sens. Appl. 2, 35–43.
24. García, G., & López, S. (2024). Artificial intelligence for sustainable agriculture: A comprehensive review. Sustainability, 17(5), 2281. https://doi.org/10.3390/su17052281
25. Evans, D. (2011). The internet of things. How the Next Evolution of the Internet is Changing Everything, Whitepaper, Cisco Internet Business Solutions Group (IBSG), Vol. 1. 1–12.
26. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.
27. Giménez-Gómez, P., Priem, N., Richardson, S., & Pamme, N. (2025). In-situ multiplexed monitoring of soil nutrients extracted with a cafeteria using a paper-based analytical device. Sensors and Actuators B: Chemical, 424, 136881. https://doi.org/10.1016/j.snb.2024.136881
28. Göl, B., & Tarhan, Ç. (2024). Tarımda dijitalleşmenin zorlukları ve AB iklim politikasında dijital tarım. Journal of Information Systems and Management Research = Bilişim Sistemleri ve Yönetim Araştırmaları Dergisi. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
29. Griffin, T., & Massey, R. (2025). Economics of drone ownership for agricultural spray applications (Publication G1274). University of Missouri Extension.
30. Gupta, G., & Pal, S. K. (2025). Applications of AI in precision agriculture. Discover Agriculture, 3, Article 61. https://doi.org/10.1007/s44279-025-00220-9
31. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
32. Guan, S., Shimazaki, Y., Takahashi, K., Kato, H., Fukami, K., & Watanabe, S. (2025). Agricultural drone-based variable-rate N application for regulating wheat protein content. Drones, 9(4), 310. https://doi.org/10.3390/drones9040310
33. Hafezalkotob, A.; Hami-Dindar, A.; Rabie, N.; Hafezalkotob, A. A decision support system for agricultural machines and equipment selection: A case study on olive harvester machines. Comp. Electron. Agric. 2018, 148, 207–216.
34. Han, R. (2024). Application of inertial navigation high precision positioning system based on SVM optimization. Syst. Soft. Comput. 6, 200105. doi: 10.1016/j.sasc.2024.200105
35. IMARC Group. (2024). Autonomous tractors market: Global industry trends, share, size, growth, opportunity and forecast 2025–2033.
36. Intergovernmental Science (IPBES). (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services (S. Díaz vd., Eds.). Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.
37. Isler, V., Xu, C., & Kong, X. (2024). Cyber-infrastructure for machine learning applications in agriculture. Frontiers in Artificial Intelligence, 7, 1496066. https://doi.org/10.3389/frai.2024.1496066
38. Jo, H.; Schimdt, N.S.; Cho, Y. An agile operations management system for green factory. Int. J. Precis. Eng. Manuf.-Green Technol. 2014, 1, 131–143.
39. Juwono, F. H., Wong, W. K., Verma, S., Shekhawat, N., Lease, B. A., Apriono, C. (2023). Machine learning for weed–plant discrimination in agriculture 5.0: An in-depth review. Artif. Intell. Agricult.
40. Karadeniz, A. T. (2024). Tarımda AI kullanımı. AgriTR Science, 6(2), 145-152.
41. Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the implementation of big data analytics in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037
42. Kage, L., Milić, V., & Andersson, M. (2025). Reinforcement learning applications in water resource management: A systematic literature review. Frontiers in Water, 7, 1537868. https://doi.org/10.3389/frwa.2025.1537868
43. Kelly, T. D., Foster, T., & Schultz, D. M. (2024). Assessing the value of deep reinforcement learning for irrigation scheduling. Smart Agricultural Technology, 7, 100403. https://doi.org/10.1016/j.atech.2024.100403
44. Kiełbasa, P.; Juliszewski, T.; Kurpaska, S. Special Issue on the Engineering of Smart Agriculture. Appl. Sci. 2023, 13, 8523.
45. Ku, K.-B., Mansoor, S., Han, G. D., Chung, Y. S., Tuan, T. T. (2023). Identification of new cold tolerant Zoysia grass species using high-resolution RGB and multi-spectral imaging. Sci. Rep. 13, 13209. doi: 10.1038/s41598-023-40128-2
46. Li, H., Zhang, Y., & Sun, M. (2024). A small autonomous field robot for strawberry harvesting. Smart Agricultural Technology, 7, 100578.
47. Li, S., Zhang, M., Ji, Y., Zhang, Z., Cao, R., Chen, B., et al. (2021). Agricultural machinery GNSS/IMU-integrated navigation based on fuzzy adaptive finite impulse response Kalman filtering algorithm. Comput. Electron. Agric. 191, 106524. doi: 10.1016/j.compag.2021.106524
48. Li, Y., & Zhao, P. (2025). Numerical modelling of a variable-rate spraying drone and evaluation of PWM-controlled nozzles. Computers and Electronics in Agriculture, 215, 108005. https://doi.org/10.1016/j.compag.2025.108005
49. Liu, C., Xu, D., Dong, X., Huang, Q. (2022). A review: Research progress of SERS-based sensors for agricultural applications. Trends Food Sci. Technol. 128, 90–101. doi: 10.1016/j.tifs.2022.07.012
50. Mark, B., & Gracias, A. (2025). Mathematical analysis of stability and bifurcation in nonlinear dynamical systems. Mathematical Reports. Retrieved from
51. Maffezzoli, F., Ardolino, M., Bacchetti, A. (2024). Maturity level and Effects of the 4.0 Paradigm on the Italian Agricultural Industry: A preliminary study. Proc. Comput. Sci. 232, 1819–1828. doi: 10.1016/j.procs.2024.02.004
52. McKinsey & Company. (2024). Global farmer insights 2024: Technology adoption and profitability.
53. Miller, C. (2024, September 18). From field to the cloud: How AI is helping regenerative agriculture to grow. Reuters.
54. Mogili, U. M. R., Deepak, B. (2018). Review on application of drone systems in precision agriculture. Proc. Comput. Sci. 133, 502–509. doi: 10.1016/j.procs.2018.07.063
55. Mor, S., Madan, S., & Prasad, K. D. (2021). Artificial Intelligence and Carbon Footprints: Roadmap for Indian Agriculture. Strategic Change, 30(3), 269–280. https://doi.org/10.1002/jsc.2409
56. Moreno, L., Rahman, A., & Li, Y. (2024). Crop yield prediction using machine learning: An extensive review. Heliyon, 10(2), e08673. https://doi.org/10.1016/j.heliyon.2024.e08673
57. Mwangi, J., Ouma, G., & Vanlauwe, B. (2025). Modelling the climate-change adaptation potential of no-tillage maize systems using APSIM. Mitigation and Adaptation Strategies for Global Change, 30(4), 62.
58. Narin, M. (2024). Tarımda Dijitalleşme: Dünyada ve Türkiye'de Bitki Fabrikaları - Digitalization in Agriculture: Plant Factories in the World and Türkiye. In International Congress on Agriculture and Food Sciences, 89–95.
59. Noble, D. W. A., Stojanovic, D., Balasubramanian, P., & Ritchie, M. G. (2025). Deep reinforcement learning for sustainable agriculture: A review. AI Perspectives, 3(1), 12–34.
60. Ntalampiras, S., & Potamitis, I. (2024). Acoustic sensor networks for pest monitoring in precision agriculture. Sensors, 24(3), 1096. https://doi.org/10.3390/s24031096
61. O’Connell, K., & Anderson, J. (2024). Smart farming technologies in the Midwest: Adoption and impacts. Agricultural Systems, 217, 103673.
62. Özbek, B., & Bilgili, M. (2025). AI-assisted pest detection and management in cotton fields. Computers and Electronics in Agriculture, 220, 107936. https://doi.org/10.1016/j.compag.2025.107936
63. Özgüven, M.M. (2018). Hassas Tarım. Akfon Yayınları, Sayfa Sayısı: 334, ISBN: 978-605-68762-4-0. Ankara. Paraforos, D. S., Theocharis, J. B., & Xydis, G. (2024). Emerging AI applications in precision viticulture: A review. Agronomy, 14(4), 823.
64. Patel, R., & Shah, S. (2024). Internet of Things-based smart agriculture: Challenges and opportunities. Journal of Agricultural Informatics, 15(1), 27–38.
65. Patel, S., & Roy, S. (2024). Predicting irrigation needs using remote sensing and machine learning. Smart Agricultural Technology, 7, 100604.
66. Perez, L. (2024). Satellite imagery and AI to monitor deforestation. Environmental Monitoring and Assessment, 196(7), 418.
67. Pfeiffer, L., & Haynes, K. (2024). Developing AI-driven water management for sustainable agriculture. Water, 16(2), 289.
68. Pham, M. H., Saleem, S. K., Okello, N. (2013). “Real-time optimization of irrigation scheduling in agriculture,” in 2013 25th Chinese Control and Decision Conference (CCDC). 4435–4439 (IEEE).
69. Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Inf. Process. Agric. 5, 21–32. doi: 10.1016/j.inpa.2017.12.002
70. Puri, V., Nayyar, A., & Raja, L. (2024). Agriculture drones: A modern tool for precision agriculture. Journal of Agricultural Studies, 12(2), 120–135.
71. Rajak, P., Ganguly, A., Adhikary, S., Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. J. Agric. Food Res. 14, 100776. doi: 10.1016/j.jafr.2023.100776
72. Rahman, S. A., Khan, S. A., Iqbal, S., Rehman, M. M., Kim, W. Y. (2023). Eco-friendly, high-performance humidity sensor using purple sweet-potato peel for multipurpose applications. Chemosensors 11, 457. doi: 10.3390/chemosensors11080457
73. Rahman, S. A., Khan, S. A., Iqbal, S., Khadka, I. B., Rehman, M. M., Jang, J., et al. (2024). Hierarchical porous biowaste-based dual humidity/pressure sensor for robotic tactile sensing, sustainable health, and environmental monitoring. Adv. Energy Sustainabil. Res. 5, 2400144. doi: 10.1002/aesr.202400144
74. Ravindran, K., Kumar, P., & Srivastava, A. (2025). AI in sustainable agricultural practices: A review. International Journal of Agricultural Management, 14(1), 75–89.
75. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv. https://arxiv.org/abs/1804.02767 Roper, J. M., Garcia, J. F., Tsutsui, H. (2021). Emerging technologies for monitoring plant health in vivo. ACS Omega. 6, 5101–5107. doi: 10.1021/acsomega.0c05850
76. Roy, S., & Patel, S. (2025). Deep learning models for plant disease diagnosis: A comprehensive review. Computers and Electronics in Agriculture, 225, 107996.
77. Saleem, M., Naveed, M., & Kiani, F. (2025). Crop disease detection using deep learning techniques: A review. Artificial Intelligence in Agriculture, 12, 1–15.
78. Samad, A., & Haider, S. (2024). Enhancing crop yield prediction with IoT and AI: Case study from Pakistan. Sensors, 24(5), 2123.
79. Saqib, M., Khan, S. A., Khan, M., Iqbal, S., Rehman, M. M., Kim, W. Y. (2024). Self-powered humidity sensor driven by triboelectric nanogenerator composed of bio-wasted peanut skin powder. Polymers. (Basel). 16, 790. doi: 10.3390/polym16060790
80. Singh, J., & Singh, D. (2024). Internet of Things and AI in modern agriculture: A review. International Journal of Agriculture and Technology, 20(4), 809–821.
81. Singh, N., & Kumar, V. (2025). Precision irrigation scheduling using reinforcement learning. Computers and Electronics in Agriculture, 220, 108003.
82. Smith, B., & Wesson, J. (2024). Robotic harvesting systems for fruit crops: Challenges and opportunities. Agricultural Robotics, 6(1), 33–47.
83. Tian, Z., & Li, Y. (2024). AI-enabled pest detection in smart greenhouses. Journal of Agricultural Science and Technology, 26(3), 55–67.
84. United Nations Environment Programme. (2023). Global environmental outlook 6: Healthy planet, healthy people. Cambridge University Press.
85. Wang, J., Zhao, X., & Liu, Z. (2025). Machine learning approaches for drought prediction in agricultural regions. Environmental Modelling & Software, 157, 105495.
86. Yang, Y., Zhang, H., & Li, J. (2024). Optimizing fertilizer use with AI-powered decision support systems. Computers and Electronics in Agriculture, 215, 108021.
87. Yilmaz, H., & Demir, M. (2025). The role of drones in modern agricultural practices: A review. Drones, 9(1), 12.
88. Yin, H., Cao, Y., Marelli, B., Zeng, X., Mason, A. J., Cao, C. (2021). Soil sensors and plant wearables for smart and precision agriculture. Adv. Mater. 33, 2007764. doi: 10.1002/adma.202007764
89. Zhang, X., Feng, G., & Sun, X. (2024). Hassas tarımda toprak nemi izlemenin ileri teknolojileri: Bir inceleme. Tarım ve Gıda Araştırmaları Dergisi, 18, 101473. https://doi.org/10.1016/j.jafr.2024.101473
90. Zuo, C., Wang, T., & Chen, M. (2024). Smart irrigation systems for water conservation in agriculture: A review. Agricultural Water Management, 280, 108278.
91. Zecha, C. W., Link, J., Claupein, W. (2013). Mobile sensor platforms: categorisation and research applications in precision farming. J. Sensors. Sensor. Syst. 2, 51–72. doi: 10.5194/jsss-2-51-2013