KaydedilenlerDerginizi ekleyin

Dergiler / Türkiye Arazi Yönetimi Dergisi / 2026 / Cilt: 8

İklim Değişikliği Bağlamında Arpa Tarlalarında İHA Tabanlı Akıllı İzleme ve Yönetim Sistemleri

UAV-Based Smart Monitoring and Management Systems in Barley Fields in the Context of Climate Change

Çağdaş Can Toprak(SIIRT UNIVERSITY, FACULTY OF AGRICULTURE)0000-0002-0951-7458
Zeki Erden(SİİRT ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ)0000-0003-1613-7768
Fatih Çığ(SİİRT ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ)0000-0002-4042-0566
Sayfa
—
Yayın Tarihi
2026-06-29
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu doğrultuda, İHA tabanlı akıllı tarım uygulamalarının arpa üretiminde çevresel streslerin izlenmesi ve yönetimi üzerindeki potansiyeli tartışılmakta, iklim değişikliğine uyumlu üretim stratejileri çerçevesinde teknolojik olanaklar ile arazi kullanımı ve mülkiyet boyutu ele alınmıştır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:İklim değişikliğinin tarımsal üretim üzerindeki etkileri, özellikle kuraklık, aşırı sıcaklık ve besin maddesi eksiklikleri gibi stres faktörlerinin sıklığı ve şiddetinin artmasıyla daha belirgin hâle gelmiştir.
📊
Temel Bulgular & Veri:Bu doğrultuda, arpa (Hordeum vulgare L.) gibi stratejik tahıl ürünlerinde sürdürülebilir üretim modellerine duyulan ihtiyaç giderek artmaktadır.
💡
Sonuç & Katkı:Bu doğrultuda, İHA tabanlı akıllı tarım uygulamalarının arpa üretiminde çevresel streslerin izlenmesi ve yönetimi üzerindeki potansiyeli tartışılmakta, iklim değişikliğine uyumlu üretim stratejileri çerçevesinde teknolojik olanaklar ile arazi kullanımı ve mülkiyet boyutu ele alınmıştır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:İklim · Arpa · Sensör

Özet

İklim değişikliğinin tarımsal üretim üzerindeki etkileri, özellikle kuraklık, aşırı sıcaklık ve besin maddesi eksiklikleri gibi stres faktörlerinin sıklığı ve şiddetinin artmasıyla daha belirgin hâle gelmiştir. Bu doğrultuda, arpa (Hordeum vulgare L.) gibi stratejik tahıl ürünlerinde sürdürülebilir üretim modellerine duyulan ihtiyaç giderek artmaktadır. Arpanın görece kuraklığa dayanıklı yapısına rağmen, uzun süreli çevresel stres koşullarının verim ve kaliteyi olumsuz etkilediği bilinmektedir. Son yıllarda tarımsal izleme ve yönetim süreçlerinde öne çıkan insansız hava araçları (İHA), özellikle kuraklık, ısı stresi ve azot eksikliği gibi sorunların erken safhada belirlenmesinde etkili araçlar olarak dikkat çekmektedir. Multispektral ve termal sensörlerle donatılan İHA sistemleri, uzaktan algılama ve yapay zekâ destekli analizlerle entegre edilerek karar destek mekanizmalarına veri sağlamaktadır. Bu doğrultuda, İHA tabanlı akıllı tarım uygulamalarının arpa üretiminde çevresel streslerin izlenmesi ve yönetimi üzerindeki potansiyeli tartışılmakta, iklim değişikliğine uyumlu üretim stratejileri çerçevesinde teknolojik olanaklar ile arazi kullanımı ve mülkiyet boyutu ele alınmıştır.

Abstract

The effects of climate change on agricultural production have become increasingly evident, particularly with the rising frequency and intensity of stress factors such as drought, extreme heat, and nutrient deficiencies. In this context, there is a growing need for sustainable production models in strategic cereal crops like barley (Hordeum vulgare L.). Although barley is relatively tolerant to drought, prolonged exposure to environmental stress conditions is known to negatively impact both yield and quality. In recent years, unmanned aerial vehicles (UAVs) have gained prominence in agricultural monitoring and management processes, offering effective tools for the early detection of critical issues such as drought, heat stress, and nitrogen deficiency. Equipped with multispectral and thermal sensors, UAV systems provide high-resolution data that can be integrated with remote sensing and artificial intelligence-based analytical tools to support decision-making processes. Accordingly, the potential of UAV-based smart farming applications for monitoring and managing environmental stresses in barley production is examined, with technological opportunities, land use, and property rights discussed within the framework of climate change–adaptive production strategies.

🔖

Bu Makaleye Atıf Yap

Toprak, Ç. C., Erden, Z., Çığ, F. (2026). İklim Değişikliği Bağlamında Arpa Tarlalarında İHA Tabanlı Akıllı İzleme ve Yönetim Sistemleri. Türkiye Arazi Yönetimi Dergisi, Cilt: 8. https://doi.org/10.51765/tayod.1750188

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (48)

  1. Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sensing, 10(7), 1091. https://doi.org/10.3390/rs10071091
  2. Al-Khatib, K., & Paulsen, G. M. (1990). Photosynthesis and productivity during high temperature stress of wheat genotypes from major world regions. Crop Science, 30(5), 1127–1132. https://doi.org/10.2135/cropsci1990.0011183X003000050034x
  3. Amani, I., Fischer, R. A., & Reynolds, M. P. (1996). Canopy temperature depression association with yield of irrigated spring wheat cultivars in a hot climate. Journal of Agronomy and Crop Science, 176(2), 119–129. https://doi.org/10.1111/j.1439-037X.1996.tb00454.x
  4. Baik, B. K., & Ullrich, S. E. (2008). Barley for food: Characteristics, improvement, and renewed interest. Journal of Cereal Science, 48(2), 233–242. https://doi.org/10.1016/j.jcs.2008.02.002
  5. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395–10412. https://doi.org/10.3390/rs61110395
  6. Blum, A. (2011). Plant Breeding for Water-Limited Environments. Springer. https://doi.org/10.1007/978-1-4419-7491-4
  7. Cabrera‐Bosquet, L., Crossa, J., von Zitzewitz, J., Serret, M. D., & Luis Araus, J. (2012). High‐throughput phenotyping and genomic selection: The frontiers of crop breeding converge F. Journal of integrative plant biology, 54(5), 312-320. https://doi.org/10.1111/j.1744-7909.2012.01116.x
  8. Clevers, J. G., & Gitelson, A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation, 23, 344-351. https://doi.org/10.1016/j.jag.2012.10.008
  9. Cho, S. B., Soleh, H. M., Choi, J. W., Hwang, W. H., Lee, H., Cho, Y. S.,. & Kim, G. (2024). Recent methods for evaluating crop water stress using ai techniques: A review. Sensors, 24(19), 6313. https://doi.org/10.3390/s24196313
  10. Demetriou, D. (2013). The development of an integrated planning and decision support system (IPDSS) for land consolidation. Springer Science & Business Media.
  11. European Commission. (2019). Commission Implementing Regulation (EU) 2019/947 of 24 May 2019 on the rules and procedures for the operation of unmanned aircraft. Official Journal of the European Union, L 152, 45–71. https://eur-lex.europa.eu/eli/reg_impl/2019/947/oj
  12. Eitel, J. U., Magney, T. S., Vierling, L. A., & Dittmar, G. (2014). Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 229-240. https://doi.org/10.1016/j.isprsjprs.2014.09.009
  13. Enemark, S. (2001). Land administration infrastructures for sustainable development. Property Management, 19(5), 366-383. https://www.emerald.com/pm/article-pdf/19/5/366/2114454/02637470110410194.pdf
  14. FAA. (2021). Summary of small unmanned aircraft rule (Part 107). Federal Aviation Administration (FAA), U.S. Department of Transportation.
  15. FAO. (2020). The State of the World’s Land and Water Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations.
  16. Furbank, R. T., & Tester, M. (2011). Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16(12), 635–644. https://doi.org/10.1016/j.tplants.2011.09.005
  17. Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542-553. https://doi.org/10.1016/S0034-4257(03)00131-7
  18. Hunt JR, E. R., Cavigelli, M., Daughtry, C. S. T., Mcmurtrey III, J., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6, 359-378. https://link.springer.com/article/10.1007/s11119-005-2324-5
  19. Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., & Price, A. H. (2009). Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Functional Plant Biology, 36(11), 978–989. https://doi.org/10.1071/FP09123
  20. IPCC. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press.
  21. Gitelson, A. A., Merzlyak, M. N., & Lichtenthaler, H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology, 148(3-4), 501-508. https://doi.org/10.1016/S0176-1617(96)80285-9
  22. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
  23. Khose, S. B., & Mailapalli, D. R. (2024). UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice. Geocarto International, 39(1), 2373867. https://doi.org/10.1080/10106049.2024.2373867
  24. Lawlor, D. W. (2002). Limitation to photosynthesis in water-stressed leaves: Stomata vs. metabolism and the role of ATP. Annals of Botany, 89(7), 871–885. https://doi.org/10.1093/aob/mcf110
  25. Leinonen, I., & Jones, H. G. (2004). Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany, 55(401), 1423–1431. https://doi.org/10.1093/jxb/erh146
  26. Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529, 84–87. https://doi.org/10.1038/nature16467
  27. Li, F., Mistele, B., Hu, Y., Chen, X., & Schmidhalter, U. (2014). Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy, 52, 198-209. https://doi.org/10.1016/j.eja.2013.09.006
  28. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
  29. Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
  30. Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
  31. Raihan, A. (2024). A systematic review of Geographic Information Systems (GIS) in agriculture for evidence-based decision making and sustainability. Global Sustainability Research, 3(1), 1-24. https://doi.org/10.56556/gssr.v3i1.636
  32. Raun, W. R., & Johnson, G. V. (1999). Improving nitrogen use efficiency for cereal production. Agronomy Journal, 91(3), 357–363. https://doi.org/10.2134/agronj1999.00021962009100030001x
  33. Rollins, J. A., Habte, E., Templer, S. E., Colby, T., Schmidt, J., & von Korff, M. (2013). Leaf proteome alterations in the context of physiological and morphological responses to drought and heat stress in barley (Hordeum vulgare L.). Journal of Experimental Botany, 64(11), 3201–3212. https://doi.org/10.1093/jxb/ert158
  34. Samarah, N. H. (2005). Effects of drought stress on growth and yield of barley. Agronomy for sustainable development, 25(1), 145-149. https://doi.org/10.1051/agro:2004064
  35. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J.,. & Pavek, M. J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112-123. https://doi.org/10.1016/j.eja.2015.07.004
  36. Simic Milas, A., Cracknell, A. P., & Warner, T. A. (2018). Drones—The third generation source of remote sensing data. International Journal of Remote Sensing, 39(21), 7125–7137. https://doi.org/10.1080/01431161.2018.1523832
  37. Sklenicka, P. (2016). Classification of farmland ownership fragmentation as a cause of land degradation: A review on typology, consequences, and remedies. Land Use Policy, 57, 694–701. https://doi.org/10.1016/j.landusepol.2016.06.032
  38. Tremblay, N., Wang, Z., Ma, B. L., Belec, C., & Vigneault, P. (2009). A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agriculture, 10, 145-161. https://link.springer.com/article/10.1007/s11119-008-9080-2
  39. Trendov, N. M., Varas, S., & Zeng, M. (2019). Digital technologies in agriculture and rural areas: Status report. Rome: Food and Agriculture Organization of the United Nations.
  40. Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., & Avizzano, C. A. (2015). Towards smart farming and sustainable agriculture with drones. In 2015 International Conference on Intelligent Environments, 140–143. IEEE. https://doi.org/10.1109/IE.2015.29
  41. Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349
  42. Wahid, A., Gelani, S., Ashraf, M., & Foolad, M. R. (2007). Heat tolerance in plants: An overview. Environmental and Experimental Botany, 61(3), 199–223. https://doi.org/10.1016/j.envexpbot.2007.05.011
  43. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
  44. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693–712. https://doi.org/10.1007/s11119-012-9274-5
  45. Zia, S., Romano, G., Spreer, W., Sanchez, C., Cairns, J., Araus, J. L., & Müller, J. (2013). Infrared thermal imaging as a rapid tool for identifying water‐stress tolerant maize genotypes of different phenology. Journal of Agronomy and Crop Science, 199(2), 75-84. https://doi.org/10.1111/j.1439-037X.2012.00537.x
  46. Xing, Y., Liu, X., & Wang, X. (2025). Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation. Frontiers in Agronomy, 7, 1670380. https://doi.org/10.3389/fagro.2025.1670380
  47. Cho, S. B., Soleh, H. M., Choi, J. W., Hwang, W. H., Lee, H., Cho, Y. S.,. & Kim, G. (2024). Recent methods for evaluating crop water stress using ai techniques: A review. Sensors, 24(19), 6313. https://doi.org/10.3390/s24196313
  48. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J.,. & Pavek, M. J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112-123. https://doi.org/10.1016/j.eja.2015.07.004