TARIMDA DİJİTAL DÖNÜŞÜM: AKILLI TARIM TEKNOLOJİLERİ VE YAPAY ZEKÂNIN KULLANIMI
TARIMDA DİJİTAL DÖNÜŞÜM: AKILLI TARIM TEKNOLOJİLERİ VE YAPAY ZEKÂNIN KULLANIMI
DOI:
https://doi.org/10.5281/zenodo.16788442Keywords:
Smart Agricultural Technologies, Artificial Intelligence, Digital Transformation in Agriculture, AgricultureAbstract
This research examines smart agricultural technologies, which are a reflection of digital transformation in the agricultural sector, with a multidimensional approach. Components such as sensors, artificial intelligence, robots, image processing systems, drone and satellite technologies developed in line with the goal of increasing the efficiency of agricultural production, optimizing resource use and ensuring sustainability form the basis of the study. The capacity of smart sensors to collect data such as soil moisture, temperature, and pH; the role of artificial intelligence in forecasting and decision support systems; the effectiveness of robot technologies in automation processes; Topics such as the contribution of image processing techniques in disease detection and product classification are discussed. In addition, the difficulties encountered in the implementation process and solution suggestions were evaluated; Elements such as legislative deficiencies, costs, technical competence and digital literacy of farmers were discussed. As a result, smart agricultural technologies create a revolutionary transformation in agricultural production; It is becoming a strategic tool in terms of food security, environmental sustainability and combating climate change. In this context, accelerating technological adaptation, increasing educational activities and providing policy support stand out as an important necessity.
References
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mustafa Enes MUKTAR

This work is licensed under a Creative Commons Attribution 4.0 International License.