AI-POWERED PRECİSİON AGRİCULTURE TECHNOLOGİES AND SUSTAİNABLE RESOURCE MANAGEMENT

Abstract

AI-supported precision agriculture technologies increase productivity in agricultural production while enabling the more efficient and sustainable use of natural resources. Through technologies such as Global Positioning System (GPS), internet of things (IoT), remote sensing, big data analytics, and automation systems, real-time data are collected from agricultural fields; based on these data, processes such as irrigation, fertilization, pesticide application, and harvesting can be carried out in a more effective and controlled manner. In this way, unnecessary use of water, soil, energy, and chemical inputs is prevented, reducing costs and minimizing environmental impacts. From the perspective of sustainable resource management, these technologies contribute to the development of production systems that are more resilient and adaptable to global challenges such as climate change, drought, and population growth. However, factors such as high costs, insufficient technical infrastructure, and low levels of digital literacy remain significant barriers limiting the widespread adoption of these technologies. Overall, AI-supported precision agriculture stands out as a key element in future agricultural systems, strengthening efficiency, sustainability, and the protection of natural resources.

Keywords

• Artificial Intelligence
• Precision Agriculture
• Sustainability

YAPAY ZEKÂ DESTEKLİ HASSAS TARIM TEKNOLOJİLERİ VE SÜRDÜRÜLEBİLİR KAYNAK YÖNETİMİ

Abstract

Yapay zekâ destekli hassas tarım teknolojileri, tarımsal üretimde verimliliği yükseltirken doğal kaynakların daha verimli ve sürdürülebilir biçimde kullanılmasını mümkün kılmaktadır. Küresel Konumlandırma Sistemi (GPS), nesnelerin interneti (IoT), uzaktan algılama, büyük veri analitiği ve otomasyon sistemleri gibi teknolojiler aracılığıyla tarım alanlarından anlık veriler elde edilmekte; bu veriler ışığında sulama, gübreleme, ilaçlama ve hasat gibi süreçler daha etkin ve kontrollü biçimde yürütülebilmektedir. Bu sayede su, toprak, enerji ve kimyasal girdilerin gereksiz kullanımının önüne geçilerek maliyetler azaltılmakta ve çevresel etkiler en düşük seviyeye indirilmektedir. Sürdürülebilir kaynak yönetimi açısından bu teknolojiler, iklim değişikliği, kuraklık ve nüfus artışı gibi küresel sorunlara karşı daha güçlü ve uyum sağlayabilen üretim sistemlerinin geliştirilmesine destek olmaktadır. Bununla birlikte yüksek maliyetler, yetersiz teknik altyapı ve düşük dijital okuryazarlık düzeyi gibi etkenler, bu teknolojilerin geniş ölçekte benimsenmesini sınırlayan önemli faktörler arasında bulunmaktadır. Genel olarak yapay zekâ destekli hassas tarım, geleceğin tarım sistemlerinde verimlilik, sürdürülebilirlik ve doğal kaynakların korunmasını güçlendiren temel unsurlardan biri olarak öne çıkmaktadır.

Keywords

• Yapay Zeka
• Hassas Tarım
• Sürdürülebilirlik

References

1. Abdelmoneim, A. A., Kimaita, H. N., Al Kalaany, C. M., Derardja, B., Dragonetti, G. ve Khadra, R. (2025). IoT sensing for advanced ırrigation management: a systematic review of trends, challenges, and future prospects. Sensors, 25, 2291. https://doi.org/10.3390/s25072291
2. Ahmed, W. A., Ojerinde, J. S., Olatoyinbo, S. F. ve Ogaleye, F. J. (2026). UAV technologies for precision agriculture: capabilities, constraints, and deployment models for smallholder systems in Sub-Saharan Africa. Drones, 10, 115. https://doi.org/10.3390/drones10020115
3. Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R. ve 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 Ali, Z., Muhammad, A., Lee, N., Waqar, M. ve Le, S. W. (2025). Artificial intelligence for sustainable agriculture: A comprehensive review of ai-driven technologies in crop production. Sustainability, 17, 2281. https://doi.org/10.3390/su17052281
4. Ali, A. ve Kaul, H. P. (2025). Monitoring yield and quality of forages and grassland in the view of precision agriculture applications-a review. Remote Sens, 17, 279. https://doi.org/10.3390/rs17020279
5. Assimakopoulos, F., Vassilakis, C., Margaris, D., Kotis, K. ve Spiliotopoulos, D. (2025). AI and related technologies in the fields of smart agriculture: A review. Information, 16, 100. https://doi.org/10.3390/info16020100
6. Awais, M., Wang, X., Hussain, S., Aziz, F. ve Mahmood, M. Q. (2025). Advancing precision agriculture through digital twins and smart farming technologies: a review. AgriEngineering, 7, 137. https://doi.org/10.3390/agriengineering7050137
7. Bahmutsky, S., Grassauer, F., Arulnathan, V. ve Pelletier, N. (2024). A review of life cycle impacts and costs of precision agriculture for cultivation of field crops. Sustainable Production and Consumption, 52, 347-362. https://doi.org/10.1016/j.spc.2024.11.010
8. Benitez-Paez, F., Brum-Bastos, V. S., Beggan, C. D., Long, J. A. ve Demšar, U. (2021). Fusion of wildlife tracking and satellite geomagnetic data for the study of animal migration. Movement Ecology, 9, 31. https://doi.org/10.1186/s40462-021-00268-4

9. Crockett E. (2023). 8 top Internet of Things (IoT) certifications, datamation. Available from URL: https://www.datamation.com/careers/iot-certifications/. (Erişim tarihi:10.06.2026).
10. Çukur, T. ve Işın, F. (2008). İzmir ili Torbalı ilçesinde sanayi domatesi üreticilerinin sürdürülebilir tarım uygulamaları. Ege Üniversitesi Ziraat Fakültesi Dergisi, 45(1), 27-36, ISSN 1018-8851.
11. Dong, J., Liang, X., Du, B., Ju, Y., Wang, Y. ve Guo, H. (2025). Applications of geographic information systems in ecological impact assessment: A methods landscape, practical bottlenecks, and future pathways. Sustainability, 17, 10358, https:/doi.org/10.3390/su172210358
12. Fountas S, Blackmore S, Ess D, Hawkins S, Blumhoff G, Lowenberg-Deboer J. ve Sorensen C. G. (2005). Farmer experience with precision agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture, 6(2), 121-141.
13. Futa, B., Gmitrowicz-Iwan, J., Skersien˙e, A., Šlepetien˙e, A. ve Parašotas, I. (2024). Innovative soil management strategies for sustainable agriculture. Sustainability, 16, 9481. https://doi.org/10.3390/su16219481
14. Güneş, E. ve Karakaş, T. (2022). Tarım ve Gıda Sistemlerinde Sürdürülebilirlik Yaklaşımları. Journal of Academic Value Studies, 8(3), 304-316.
15. Heil, K. ve Schmidhalter, U. (2017). The application of EM38: Determination of soil parameters, selection of soil sampling points and use in agriculture and archaeology. Sensors, 17, 2540. https:/doi.org/10.3390/s17112540
16. Kabato, W., Hailegnaw, N., Mutum, L. ve Molnar, Z. (2025). Managing soil health for climate resilience and crop productivity in a changing environment. Science of the Total Environment, 1000, 180460. https://doi.org/10.1016/j.scitotenv.2025.180460
17. Karydas, C., Iatrou, M. ve Mourelatos, S. (2025). An ınnovative process chain for precision agriculture services. Computers, 14, 234. https://doi.org/10.3390/computers14060234
18. Kendall, H., Clark, B., Li, W., Jin, S., Jones, G. D., Chen, J., Taylor, J., Li, Z. ve Frewer, L. J. (2022). Precision agriculture technology adoption: A qualitative study of small-scale commercial ‘‘Family Farms’’ located in The North China plain. Precision Agriculture, 23, 319-351. https://doi.org/10.1007/s11119-021-09839-2
19. Kosti´c, M., Šarac, V., Narandži´c, T. ve Kovaˇcevi´c, D. B. (2026). Digital and green technological drivers of transformation in the agri-food sector. Foods, 15, 1081. https://doi.org/10.3390/foods15061081
20. Lan, J. ve Ban, Q. (2025). The farm-level economic and environmental benefits of precision agriculture technology adoption: A meta-analysis of global evidence. Sustainability, 17, 11223. https://doi.org/10.3390/su172411223
21. Li, S., Xu L. D. ve Zhao S. (2015). The internet of things: A survey. Information Systems Frontier, 17, 243-259. https://doi.org/10.1007/s10796-014-9492-7
22. Liu, Q., Yu, R., Suo, H., Cai, Y., Chen, L. ve Jiang, H. (2025). Autonomous driving in agricultural machinery: Advancing the frontier of precision agriculture. Actuators, 14, 464. https://doi.org/10.3390/act14090464
23. Maddison, R. ve Mhurchu, C. N. (2009). Global positioning system: A new opportunity in physical activity measurement. International Journal of Behavioral Nutrition and Physical Activity, 6, 73. https:/doi.org/10.1186/1479-5868-6-73
24. Makapela, M., Alexander, G. ve Tshelane, M. (2025). Enhancing agricultural productivity among emerging farmers through data-driven practices. Sustainability, 17, 4666. https://doi.org/10.3390/su17104666
25. Mamabolo, E., Mashala, M. J., Mugari, E., Mogale, T. E., Mathebula, N., Mabitsela, K. ve Ayisi, K. K. (2025). Application of precision agriculture technologies for crop protection and soil health. Smart Agricultural Technology, 12, 101270. https://doi.org/10.1016/j.atech.2025.101270
26. Martinho, V. J. P. D. ve Guiné, R. D. P. F. (2021). Integrated-smart agriculture: Contexts and assumptions for a broader concept. Agronomy, 11, 1568. https:/doi.org/10.3390/agronomy11081568
27. McBratney, A. B. ve Pringle, M. J. (1999). Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture, 1(2), 125-152.
28. Meng, Y. ve Li, D. (2025). Digital pathways to sustainable agriculture: Examining the role of agricultural digitalization in green development in China. Sustainability, 17, 3652. https://doi.org/10.3390/su17083652
29. Nsoh, B., Katimbo, A., Guo, Hç, Heeren, D. M., Nakabuye, H. N., Qiao, X., Ge, Y., Rudnick, D. R., Wanyama, J., Bwambale, E. ve Kiraga, S. (2024). Internet of things-based automated solutions utilizing machine learning for smart and real-time ırrigation management: A review. Sensors, 24, 7480. https://doi.org/10.3390/s24237480
30. Öz, A. (2025). Nesnelerin İnterneti Teknolojilerini Kullanan Tüketicilerin Paylaşım Ekonomisine Katılma Niyetlerinin Belirlenmesi Üzerine Bir Araştırma. Harran Üniversitesi Sosyal Bilimler Enstitüsü, Doktora Tezi, Şanlıurfa, 2025.
31. Radoˇcaj, D., Plašˇcak, I., Heffer, G. ve Juriši´c, M. (2022). A low-cost global navigation satellite system positioning accuracy assessment method for agricultural machinery. Applied Sciences 12(2), 693. https://doi.org/10.3390/app12020693
32. Rejeb, A., Rejeb, K., Abdollahi, A. ve Hassoun, A. (2024). Precision agriculture: A bibliometric analysis and research agenda. Smart Agricultural Technology, 9, 100684. https://doi.org/10.1016/j.atech.2024.100684
33. Reza, M. N., Lee, K. -H., Karim, M. R., Haque, M. A., Bicamumakuba, E., Dey, P. K., Jang, Y. Y. ve Chung, S. -O. (2025). Trends of soil and solution nutrient sensing for open field and hydroponic cultivation in facilitated smart agriculture. Sensors, 25, 453. https://doi.org/10.3390/s25020453
34. Ronzhin, A., Figurek, A., Surovtsev, V. ve Dibirova, K. (2025). Digital transformation and precision farming as catalysts of rural development. Land, 14, 1464. https://doi.org/10.3390/land14071464
35. Šarauskis, E., Kazlauskas, M., Naujokien˙e, V., Bruˇcien˙e, I., Steponaviˇcius, D., Romaneckas, K. ve Jasinskas, A. (2022). Variable rate seeding in precision agriculture: Recent advances and future perspectives. Agriculture, 12, 305. https://doi.org/10.3390/agriculture12020305
36. Tal, A. (2025). The environmental ımpacts of overpopulation. Encyclopedia, 5, 45. https://doi.org/10.3390/encyclopedia5020045
37. Terán-Samaniego, K., Robles-Parra, J. M., Vargas-Arispuro, I., Martínez-Téllez, M. Á., Garza-Lagler, M. C., Félix-Gurrlola, D., Maycotte-de la Peña, M. L., Tafolla-Arellano, J. C., García-Figueroa, J. A. ve Espinoza-López, P. C. (2025). Agroecology and sustainable agriculture: Conceptual challenges and opportunities-a systematic literature review. Sustainability, 17, 1805. https://doi.org/10.3390/su17051805
38. Thilakarathne, N. N., Abu Bakar, M. S., Abas, P. E. ve Yassin, H. (2025). Internet of things enabled smart agriculture: current status, latest advancements, challenges and countermeasures. Heliyon, 11, e42136. https://doi.org/10.1016/j.heliyon.2025.e42136
39. Türker U. (2001). Hassas tarım tekniği. Türk-Koop Ekin, 16, 100-106.
40. Vărzaru, A. A. (2025). Digital revolution in agriculture: Using predictive models to enhance agricultural performance through digital technology. Agriculture, 15, 258. https://doi.org/10.3390/agriculture15030258
41. Vatandaş, M., Güner, M., ve Türker, U. (2005). Hassas tarım teknolojileri”, TMMOB Ziraat Mühendisleri Odası 6. Teknik Kongresi, Ankara, Türkiye, 3-7 Ocak 2005, Kitap sayfası, 347-365.
42. Yin, W., Yang, X., Liu, W. (2025). Sustainable management and regulation of agriculturalwater resources in the context of global climate change. Sustainability, 17, 2760. https://doi.org/10.3390/su17062760
43. Zhang, Q. F. (2024). From sustainable agriculture to sustainable agrifood systems: A comparative review of alternative models. Sustainability, 16, 9675. https://doi.org/10.3390/su16229675