Derleme
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Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri

Yıl 2025, Cilt: 12 Sayı: 3, 348 - 364, 31.10.2025
https://doi.org/10.19159/tutad.1730333

Öz

Bu derlemede, tarla bitkileri üretiminde yaygın olarak kullanılan coğrafi bilgi sistemleri, uzaktan algılama, insansız hava araçları, değişken oranlı uygulama sistemleri, verim izleme teknolojileri, tarımsal robotlar, yapay zekâ, makine öğrenmesi, nesnelerin interneti ve dijital görüntü işleme gibi ileri teknolojiler ele alınmakta ve bu teknolojilerin tarımsal üretim süreçlerine sağladığı katkılar örnek uygulamalar aracılığıyla incelenmektedir. Tarım sektörü, artan küresel nüfus, iklim değişikliği, doğal kaynakların sınırlılığı ve girdi maliyetlerindeki artış gibi çeşitli zorluklarla karşı karşıyadır. Bu bağlamda, üretimin sürdürülebilirliğini sağlamak ve kaynak kullanımını en verimli şekilde yönetmek amacıyla akıllı tarım teknolojilerinin entegrasyonu kaçınılmaz hâle gelmiştir. Mevcut bulgular, akıllı tarım teknolojilerinin verimliliği artırdığını, üretim maliyetlerini optimize ettiğini, çevresel etkileri ve iş gücüne olan bağımlılığı azalttığını göstermektedir. Ayrıca bu teknolojilerin, tarımsal karar destek sistemlerini güçlendirdiği ve sürdürülebilir kalkınma hedefleriyle uyumlu üretim modellerinin geliştirilmesine önemli katkılar sunduğu görülmektedir. Elde edilen sonuçlar, tarla bitkileri yetiştiriciliğinde dijital dönüşümün gerekliliğini ortaya koymakta ve bu teknolojilerin yaygınlaşmasının tarımsal rekabet gücünü artırma potansiyeline sahip olduğunu göstermektedir.

Kaynakça

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Smart Agricultural Technologies Used in the Field Crops

Yıl 2025, Cilt: 12 Sayı: 3, 348 - 364, 31.10.2025
https://doi.org/10.19159/tutad.1730333

Öz

This review discusses advanced technologies widely used in field crop production, including geographic information systems, remote sensing, unmanned aerial vehicles, variable rate application systems, yield monitoring technologies, agricultural robots, artificial intelligence, machine learning, the Internet of Things, and digital image processing. It examines the contributions of these technologies to agricultural production processes through practical applications. The agricultural sector faces various challenges such as the growing global population, climate change, limited natural resources, and increasing input costs. In this context, the integration of smart agricultural technologies has become inevitable to ensure the sustainability of production and to manage resource utilization in the most efficient way. Current findings indicate that smart agricultural technologies increase productivity, optimize production costs, and reduce environmental impacts as well as dependency on labor. Furthermore, these technologies strengthen agricultural decision support systems and contribute significantly to the development of production models aligned with sustainable development goals. The findings emphasize the necessity of digital transformation in field crop cultivation and demonstrate that the widespread adoption of these technologies has the potential to enhance agricultural competitiveness.

Kaynakça

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  • Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I., 2020. A compilation of UAV applications for precision agriculture. Computer Networks, 172: 107148.
  • Raihan, A., 2023. A comprehensive review of artificial intelligence and machine learning applications in energy sector. Journal of Technology Innovations and Energy, 2(4): 1-26.
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  • Raihan, A., Tuspekova, A., 2022. The nexus between economic growth, energy use, urbanization, tourism, and carbon dioxide emissions: New insights from Singapore. Sustainability Analytics and Modeling, 2: 100009.
  • Richard, K., Abdel-Rahman, E.M., Subramanian, S., Nyasani, J.O., Thiel, M., Jozani, H., Borgemeister, C., Landmann, T., 2017. Maize cropping systems mapping using rapideye observations in agro-ecological landscapes in Kenya. Sensors, 17(11): 2537.
  • Riedell, W.E., Osborne, S.L., Hesler, L.S., 2004. Insect pest and diseasedetection using remote sensing techniques. Proceedings of 7th International Conference on Precision Agriculture, 25-28 July, USA, pp. 1380-1387.
  • Roy, S., Hore, J., Sen, P., Salma, U., 2023. Hyperspectral remote sensing and its application in pest and disease management in agriculture. Indian Farmer, 10(5): 229-232.
  • Sahni, R.K., Kumar, S.P., Thorat, D., Rajwade, Y., Jyoti, B., Ranjan, J., Anand, R., 2024. Drone spraying system for efficient agrochemical application in precision agriculture. In: S.S. Chouhan, U.P. Singh and S. Jain (Eds.), Applications of Computer Vision and Drone Technology in Agriculture 4.0, Springer, Singapore, pp. 225-244.
  • Samson, G., Tremblay, N., Dudelzak, A.E., Babichenko, S.M., Dextraze, L., Wollring, J., 2000. Nutrient stress of corn plants: Early detection and discrimination using a compact multiwavelength fluorescent lidar. Proceedings of the 20th EARSeL Symposium, June 16-17, Dresden, Germany, pp. 214-223.
  • Shaheb, M.R., Venkatesh, R., Shearer, S.A., 2021. A review on the effect of soil compaction and its management for sustainable crop production. Journal of Biosystems Engineering, 46: 417-439.
  • Shearer, S.A., Fulton, J.P., Mcneill, S.G., Higgins, S.F., Engineering, A., 2002. Elements of Precision Agriculture: Basics of Yield Monitor Installation and Operation. College of Agriculture, University of Kentucky, Extension Publications, Publication ID: PA-1, (https://publications.mgcafe.uky.edu/ sites/publications.ca.uky.edu/files/pa1.pdf).
  • Shokr, M.S., Abdellatif, M.A., El Baroudy, A.A., Elnashar, A., Ali, E.F., Belal, A.A., Attia, W., Ahmed, M., Aldosari, A.A., Szantoi, Z., Jalhoum, M.E., Kheir, A.M., 2021. Development of a spatial model for soil quality assessment under arid and semi-arid conditions. Sustainability, 13(5): 2893.
  • Singh, U.P., Chouhan, S.S., Jain, S., Jain, S., 2019. Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access, 7: 43721-43729.
  • Singha, C., Swain, K.C., Swain, S.K., 2020. Best crop rotation selection with GIS-AHP technique using soil nutrient variability. Agriculture, 10(6): 213.
  • Sood, K., Singh, S., Rana, R.S., Rana, A., Kalia, V., Kaushal, A., 2015. Application of GIS in precision agriculture. Proceedings of the National Seminar on Precision Farming Technologies for High Himalayas, October 4-5, India, pp. 4-5.
  • Song, Y., Bi, J., Wang, X., 2024. Design and implementation of intelligent monitoring system for agricultural environment in IoT. Internet of Things, 25: 101029.
  • Souza, E.G., Bazzi, C.L., Khosla, R., Uribe-Opazo, M.A., Reich, R.M., 2016. Interpolation type and data computation of crop yield maps is important for precision crop production. Journal of Plant Nutrition, 39(4): 531-538.
  • Stroppiana, D., Boschetti, M., Brivio, P.A., Bocchi, S., 2009. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research, 111(1-2): 119-129.
  • Su, J., Zhu, X., Li, S., Chen, W.H., 2023. AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture. Neurocomputing, 518: 242-270.
  • Symeonaki, E., Arvanitis, K., Piromalis, D., 2020. A context-aware middleware cloud approach for integrating precision farming facilities into the IoT toward agriculture 4.0. Applied Sciences, 10(3): 813.
  • Talavera, J.M., Tobón, L.E., Gómez, J.A., Culman, M.A., Aranda, J.M., Parra, D.T., Garreta, L.E., 2017. Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142: 283-297.
  • Talaviya, T., Shah, D., Patel, N., Yagnik, H., Shah, M., 2020. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4: 58-73.
  • Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann J., Mahlein, A.K., 2018. Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. Journal of Plant Diseases and Protection, 125: 5-20.
  • Tian, H., Wang, T., Liu, Y., Qiao, X., Li, Y., 2020. Computer vision technology in agricultural automation-A review. Information Processing in Agriculture, 7(1): 1-19.
  • Tilling, A.K., O’Leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., Belford, R., 2007. Remote sensing of nitrogen and water stress in wheat. Field Crops Research, 104(1-3): 77-85.
  • Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Burschka, D., 2012. Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE Robotics & Automation Magazine, 19(3): 46-56.
  • Toscano, P., Castrignanò, A., Di Gennaro, S.F., Vonella, A.V., Ventrella, D., Matese, A., 2019. A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping. Agronomy, 9(8): 437.
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  • Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C., 2017. Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164: 31-48.
  • Velusamy, P., Rajendran, S., Mahendran, R.K., Naseer, S., Shafiq, M., Choi, J.G., 2021. Unmanned Aerial Vehicles (UAV) in precision agriculture: Applications and Challenges, Energies, 15(1): 217.
  • Voumik, L.C., Mimi, M.B., Raihan, A., 2023. Nexus between urbanization, industrialization, natural resources rent, and anthropogenic carbon emissions in South Asia: CS-ARDL approach. Anthropocene Science, 2(1): 48-61.
  • Warpe, S.T., Pippal, R.S., 2016. A study of fertilizer distribution system for agriculture using wireless sensor network. International Journal of Computer Applications, 147(2): 43-46.
  • Wei, Z., Fang, W., 2024. UV-NDVI for real-time crop health monitoring in vertical farms. Smart Agricultural Technology, 8: 100462.
  • Wong, T.F.M., Stone, P.J., Lyle, G., Wittwer, K., 2004. PA for all-Is it the journey, destination or mode of transport that’s most important? Proceedings of 7th International Conference on Precision Agriculture, 25-28 July, USA, pp. 576-585.
  • Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A.B., Qin, X., Yan, N., Chang, S., Zhao, Y., Dong, Q., Boken, V., Plotnikov, D., Guo, H., Wu, F., Zhao, H., Deronde, B., Tits, L., Loupian, E., 2023. Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review, 10(4): nwac290.
  • Xie, C., Shao, Y., Li, X., He, Y., 2015. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports, 5(1): 16564.
  • Yaman, H., Sungur, O., Dulupçu, M.A., 2021. Dünyada tarım ve hayvancılığın dönüşümü: teknolojiye dayalı uygulamalar ve devrimler. Tarım Ekonomisi Dergisi, 27(2): 123-133.
  • Yang, C.C., Prasher, S.O., Enright, P., Madramootoo, C., Burgess, M., Goel, P.K., Callum, I., 2003. Application of decision tree technology for image classification using remote sensing data. Agricultural Systems, 76(3): 1101-1117.
  • Yıldız, B.İ., Karabağ, K., 2025. Deep learning approaches for image-based classification of honey bee (Apis mellifera) lineages. Turkish Journal of Agricultural Research, 12(2): 224-230.
  • Yuan, Y., Miao, Y., Yuan, F., Ata-UI-Karim, S.T., Liu, X., Tian, Y., Zhu, Y., Cao, W. Cao, Q., 2022. Delineating soil nutrient management zones based on optimal sampling interval in medium-and small-scale intensive farming systems. Precision Agriculture, 23(2): 538-558.
  • Yusupova, F.E., 2024. Effectiveness of using GIS technologies in agriculture. Czech Journal of Multidisciplinary Innovations, 25: 5-10.
  • Zhang, C., Kovacs, J.M., 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13: 693-712.
  • Zhang, R., Hao, F., Sun, X., 2017. The design of agricultural machinery service management system based on Internet of Things. Procedia Computer Science, 107: 53-57.
  • Zhang, S., Wang, Y., Zhu, Z., Li, Z., Du, Y., Mao, E., 2018. Tractor path tracking control based on binocular vision. Information Processing in Agriculture, 5(4): 422-432.
  • Zhang, X., Sun, Y., Shang, K., Zhang, L., Wang, S., 2016. Crop classification based on feature band set construction and object-oriented approach using hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9): 4117-4128.
  • Zhao, G., Zhang, Y., Lan, Y., Deng, J., Zhang, Q., Zhang, Z., Li, Z., Liu, L., Huang, X., Ma, J., 2023. Application progress of UAV-LARS in identification of crop diseases and pests. Agronomy, 13(9): 2232.
  • Zhao, Y., Liu, L., Xie, C., Wang, R., Wang, F., Bu, Y., Zhang, S., 2020. An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild. Applied Soft Computing, 89: 106128.
  • Zhou, G., Zhang, W., Chen, A., He, M., Ma, X., 2019. Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE Access, 7: 143190-143206.
Toplam 144 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tarla Bitkileri Yetiştirme ve Islahı (Diğer)
Bölüm Derleme
Yazarlar

Sedat Severoğlu 0000-0002-9164-6557

Gönderilme Tarihi 30 Haziran 2025
Kabul Tarihi 14 Ekim 2025
Yayımlanma Tarihi 31 Ekim 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 12 Sayı: 3

Kaynak Göster

APA Severoğlu, S. (2025). Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri. Türkiye Tarımsal Araştırmalar Dergisi, 12(3), 348-364. https://doi.org/10.19159/tutad.1730333
AMA Severoğlu S. Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri. TÜTAD. Ekim 2025;12(3):348-364. doi:10.19159/tutad.1730333
Chicago Severoğlu, Sedat. “Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri”. Türkiye Tarımsal Araştırmalar Dergisi 12, sy. 3 (Ekim 2025): 348-64. https://doi.org/10.19159/tutad.1730333.
EndNote Severoğlu S (01 Ekim 2025) Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri. Türkiye Tarımsal Araştırmalar Dergisi 12 3 348–364.
IEEE S. Severoğlu, “Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri”, TÜTAD, c. 12, sy. 3, ss. 348–364, 2025, doi: 10.19159/tutad.1730333.
ISNAD Severoğlu, Sedat. “Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri”. Türkiye Tarımsal Araştırmalar Dergisi 12/3 (Ekim2025), 348-364. https://doi.org/10.19159/tutad.1730333.
JAMA Severoğlu S. Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri. TÜTAD. 2025;12:348–364.
MLA Severoğlu, Sedat. “Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri”. Türkiye Tarımsal Araştırmalar Dergisi, c. 12, sy. 3, 2025, ss. 348-64, doi:10.19159/tutad.1730333.
Vancouver Severoğlu S. Tarla Bitkilerinde Kullanılan Akıllı Tarım Teknolojileri. TÜTAD. 2025;12(3):348-64.

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