TARIMDA DİJİTAL DÖNÜŞÜM: AKILLI TARIM TEKNOLOJİLERİ VE YAPAY ZEKÂNIN KULLANIMI: TARIMDA DİJİTAL DÖNÜŞÜM: AKILLI TARIM TEKNOLOJİLERİ VE YAPAY ZEKÂNIN KULLANIMI

Mustafa Enes MUKTAR

doi:10.5281/zenodo.16788442

Yazarlar

Mustafa Enes MUKTAR TTürkiye Tarım Kredi Kooperatifleri, 2532 Sayılı Kırıkhan Tarım Kredi Kooperatifi https://orcid.org/0000-0001-6022-3116

DOI:

https://doi.org/10.5281/zenodo.16788442

Anahtar Kelimeler:

Akıllı tarım teknolojileri, yapay zekâ, tarımda dijital dönüşüm, tarım

Özet

Bu araştırma, tarım sektöründeki dijital dönüşümün bir yansıması olan akıllı tarım teknolojilerini çok boyutlu bir yaklaşımla incelemektedir. Tarım üretiminin verimliliğini artırmak, kaynak kullanımını optimize etmek ve sürdürülebilirliği sağlamak amacıyla geliştirilen sensörler, yapay zekâ, robotlar, görüntü işleme sistemleri, drone ve uydu teknolojileri gibi bileşenler çalışmanın temelini oluşturmaktadır. Akıllı sensörlerin toprak nemi, sıcaklık ve pH gibi verileri toplama kapasitesi; yapay zekânın tahmin ve karar destek sistemlerindeki rolü; robot teknolojilerinin otomasyon süreçlerindeki etkinliği; görüntü işleme tekniklerinin hastalık tespiti ve ürün sınıflandırmasında katkısı gibi konular ele alınmaktadır. Ayrıca, uygulama sürecinde karşılaşılan zorluklar ve çözüm önerileri değerlendirilmiş; mevzuat eksiklikleri, maliyetler, teknik yeterlilik ve çiftçilerin dijital okuryazarlığı gibi unsurlar ele alınmıştır. Sonuç olarak, akıllı tarım teknolojileri tarımsal üretimde devrim niteliğinde bir dönüşüm yaratmaktadır; gıda güvenliği, çevresel sürdürülebilirlik ve iklim değişikliğiyle mücadele açısından stratejik bir araç haline gelmektedir. Bu bağlamda, teknolojik adaptasyonun hızlandırılması, eğitim faaliyetlerinin artırılması ve politika desteğinin sağlanması önemli bir gereklilik olarak öne çıkmaktadır.

Referanslar

Agritechdigest, (2025). https://agritechdigest.com/top-livestock-technologies-improving-animal-husbandry/ (Access Date: July 2025).

Alex, N., Sobin, C. C., & Ali, J. (2023). A Comprehensive Study on Smart Agriculture Applications in India. Wireless Personal Communications, 129(4), 2345–2385. https://doi.org/10.1007/s11277-023-10234-5

Amarasingam, N., Musthafa, M. M., Mohamed Najim, M. M. M., & Baig, M. B. (2024). Applications of Smart Agriculture in Irrigation Water Management in Developing Countries: The Way Forward (pp. 395–421). https://doi.org/10.1007/978-3-031-65968-3_18

Amirinezhadfard, E., Niazi Tabar, A., Bashir, M., & Yang, W.-C. (2025). Plant osmosensors in next-generation smart agriculture: From innovation to application. Industrial Crops and Products, 234. https://doi.org/10.1016/j.indcrop.2025.121607

Bhuyan, A. S., Beg, M. S., Farooq, S. A., & Haq, S. U. I. (2024). Deep Learning and Blockchain Application in Smart Agriculture and Farming (pp. 145–163). https://doi.org/10.4018/979-8-3693-9879-1.ch006

Botero-Valencia, J. S., Mejia-Herrera, M., & Pearce, J. M. (2022). Low cost climate station for smart agriculture applications with photovoltaic energy and wireless communication. HardwareX, 11. https://doi.org/10.1016/j.ohx.2022.e00296

Chen, J., Wang, G., Hamani, A. K. M., Abubakar, A. S., Sun, W., Zhang, Y., Liu, Z., & Gao, Y. (2021). Optimization of nitrogen fertilizer application with climate-smart agriculture in the north china plain. Water (Switzerland), 13(23). https://doi.org/10.3390/w13233415

Chen, Z., Zhang, X., Chen, S., & Zhong, F. (2021). A Sparse Deep Transfer Learning Model and Its Application for Smart Agriculture. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/9957067

Cheng, P., Wang, S., Zhu, Y., Cui, C., & Pan, J. (2023). Application of Three-Dimensional Fluorescence Spectroscopy in Smart Agriculture—Detection of Oil Pollutants in Water. International Journal of Pattern Recognition and Artificial Intelligence, 37(3). https://doi.org/10.1142/S0218001423550042

Feng, Y. (2022). Application of Edge Computing and Blockchain in Smart Agriculture System. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/7198624

Fizza, K., Prakash Jayaraman, P. P., Banerjee, A., Georgakopoulos, D., & Ranjan, R. (2022). Evaluating Sensor Data Quality in Internet of Things Smart Agriculture Applications. IEEE Micro, 42(1), 51–60. https://doi.org/10.1109/MM.2021.3137401

Gan, S., Zhou, D., Cui, Y., & Lv, J. (2023). A Neural Network-based Approach for Apple Leaf Disease Detection in Smart Agriculture Application. International Journal of Advanced Computer Science and Applications, 14(11), 568–573. https://doi.org/10.14569/IJACSA.2023.0141158

Karam, K., Mansour, A., Khaldi, M., Clement, B., & Ammad-Uddin, M. (2024). Quadcopters in Smart Agriculture: Applications and Modelling. Applied Sciences (Switzerland), 14(19). https://doi.org/10.3390/app14199132

Kumari, N., & Praveen, N. (2024). Deep Learning and Blockchain Application in Smart Agriculture and Farming (pp. 165–187). https://doi.org/10.4018/979-8-3693-9879-1.ch007

Li, D., Li, Y., & Zhang, Z. (2024). Analysis of convolutional neural networks-based approaches in fruit disease detection for smart agriculture applications. Journal of Optics (India), 53(5), 4256–4265. https://doi.org/10.1007/s12596-023-01592-1

Li, X., Cao, Y., Yu, X., Xu, Y., Yang, Y., Liu, S., Cheng, T., & Wang, Z. L. (2022). Breeze-driven triboelectric nanogenerator for wind energy harvesting and application in smart agriculture. Applied Energy, 306. https://doi.org/10.1016/j.apenergy.2021.117977

Lu, J., Shiraishi, N., Imaizumi, R., Zhang, L., & Kimura, M. (2024). Process Development of a Liquid-Gated Graphene Field-Effect Transistor Gas Sensor for Applications in Smart Agriculture. Sensors, 24(19). https://doi.org/10.3390/s24196376

Luo, M. (2025). The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model. International Journal for Simulation and Multidisciplinary Design Optimization, 16. https://doi.org/10.1051/smdo/2025006

Maddikunta, P. K., Hakak, S., Alazab, M., Bhattacharya, S., Thippa Reddy, T. R., Khan, W. Z., & Pham, Q.-V. (2021). Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges. IEEE Sensors Journal, 21(16), 17608–17619. https://doi.org/10.1109/JSEN.2021.3049471

Mahlous, A. R. (2024). Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application. Computers, Materials and Continua, 79(3), 4781–4803. https://doi.org/10.32604/cmc.2024.050821

Mandrone, M., Chiocchio, I., Barbanti, L., Tomasi, P., Tacchini, M., & Poli, F. (2021). Metabolomic Study of Sorghum (Sorghum bicolor) to Interpret Plant Behavior under Variable Field Conditions in View of Smart Agriculture Applications. Journal of Agricultural and Food Chemistry, 69(3), 1132–1145. https://doi.org/10.1021/acs.jafc.0c06533

Marković, D., Stamenković, Z., Đorđević, B., & Randjić, S. (2024). Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing. Sensors, 24(18). https://doi.org/10.3390/s24185965

Miles, B., Bourennane, E.-B., Boucherkha, S., & Chikhi, S. (2020). A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Computer Communications, 164, 148–157. https://doi.org/10.1016/j.comcom.2020.10.009

Mowla, M. N., Mowla, N., Shah, A. F. M. S., Rabie, K. M., & Shongwe, T. (2023). Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey. IEEE Access, 11, 145813–145852. https://doi.org/10.1109/ACCESS.2023.3346299

Ömercikoğlu, M. A. Y. (2023). Smart agriculture applications and data analysis with Industry 4.0 (Master's thesis, Sakarya University). Sakarya University Institute of Science.

Papazoglou, P., Navrozidis, I., Testempasis, S., Pantazi, X. E., Lagopodi, A., & Alexandridis, T. (2025). Early detection of bacterial canker in tomato plants using spectroscopy for smart agriculture applications. Biosystems Engineering, 251, 1–10. https://doi.org/10.1016/j.biosystemseng.2025.01.009

Rehman, Z. U., Khan, M. A., Ahmed, F., Damasevicius, R., Naqvi, S. R., Nisar, W., & Javed, K. (2021). Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture. IET Image Processing, 15(10), 2157–2168. https://doi.org/10.1049/ipr2.12183

Roman, A., Rahman, M. M., Haider, S. A., Akram, T., & Naqvi, S. R. (2025). Integrating Feature Selection and Deep Learning: A Hybrid Approach for Smart Agriculture Applications. Algorithms, 18(4). https://doi.org/10.3390/a18040222

Siropyan, M. (2022). Smart farming applications (Master's thesis, Galatasaray University). Galatasaray University Institute of Science.

Tang, W., Shi, Z., Chang, F., Zhang, Z., Li, J., Jia, Y., Jiang, W., Qiu, Y., Miao, J., & Han, X. (2025). Design and Application of Wearable Sensors and Machine Learning-Integrated Internet of Things (IoT) for Smart Agriculture (Vol. 49, pp. 49–85). https://www.com/inward/record.uri?eid=2-s2.0-105007671844&partnerID=40&md5=8e5227d74615902a6b04ee876d4b5bc9

Ullo, S. L., & Sinha, G. R. (2021). Advances in IoT and Smart Sensors for Remote Sensing and Agriculture Applications. Remote Sensing, 13(13). https://doi.org/10.3390/rs13132585

Wang, Z., Liu, X., Yue, M., Yao, H., Tian, H., Sun, X., Wu, Y., Huang, Z., Ban, D., & Zheng, H. (2022). Hybridized energy harvesting device based on high-performance triboelectric nanogenerator for smart agriculture applications. Nano Energy, 102. https://doi.org/10.1016/j.nanoen.2022.107681

Yu, B., Bi, X., Liu, X., Sun, H., & Buysse, J. (2024). Exploring the application and decision optimization of climate-smart agriculture within land-energy-food-waste nexus. Sustainable Production and Consumption, 50, 536–555. https://doi.org/10.1016/j.spc.2024.08.019

DIGITAL TRANSFORMATION IN AGRICULTURE: USE OF SMART AGRICULTURAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE