زارع، قدسیه، داورپناه، مجتبی، و سالارپور، ماشااله. (1400). بررسی عوامل مؤثر بر پذیرش فناوریهای نوین آبیاری در بین کشاورزان منطقهٔ سیستان. مجله اقتصاد کشاورزی ایران، 8(4)، 23-32.
https://www.sid.ir/paper/1053406/fa
Abuzanouneh, K. I. M., Al‑Wesabi, F. N., Albraikan, A. A. R., Al Duhayyim, M., & Hamza, M. A. (2022). Design of Machine Learning Based Smart Irrigation System for Precision Agriculture. Computers, Materials & Continua, 72(1).
https://doi.org/10.32604/cmc.2022.022648
Ali, A., Hussain, T., & Zahid, A. (2025). Smart irrigation technologies and prospects for enhancing water use efficiency for sustainable agriculture. AgriEngineering, 7(4), 106.
https://www.mdpi.com/2624-7402/7/4/106
Anjum, M. N., Cheema, M. J. M., Hussain, F., & Wu, R. S. (2023). Precision irrigation: Challenges and opportunities. In Precision Agriculture: Evolution, Insights and Emerging Trends (pp. 85–101). Elsevier.
https://www.sciencedirect.com/science/article/pii/B9780443189531000076
Angelakis, A. N., Zaccaria, D., Krasilnikoff, J., Salgot, M., Bazza, M., Roccaro, P., ... & Fereres, E. (2020). Irrigation of World Agricultural Lands: Evolution through the Millennia. Water, 12(5), 1285.
https://www.mdpi.com/2073-4441/12/5/1285
Bayar, J., Ali, N., Cao, Z., Ren, Y., & Dong, Y. (2025). Artificial intelligence of things (AIoT) for precision agriculture: Applications in smart irrigation, nutrient and pest management. Smart Agricultural Technology, 5, 100860.
https://www.sciencedirect.com/science/article/pii/S2772375525008603
Colizzi, L., Dimauro, G., Guerriero, E., & Lomonte, N. (2025). Artificial intelligence and IoT for water saving in agriculture: A systematic review. Smart Agricultural Technology, 11, 101008.
https://www.sciencedirect.com/science/article/pii/S2772375525002412
Del-Coco, M., Leo, M., & Carcagnì, P. (2024). Machine learning for smart irrigation in agriculture: How far along are we? Information, 15(6), 306.
https://www.mdpi.com/2078-2489/15/6/306
Elshaikh, A., Elsiddig Elsheikh, & Mabrouki, J. (2024). Applications of Artificial Intelligence in Precision Irrigation. Journal of Environmental & Earth Sciences, 6(2), 176–186.
https://nchr.elsevierpure.com/en/publications/applications-of-artificial-intelligence-in-precision-irrigation/
Fader, M., Gerten, D., Krause, M., Lucht, W., & Cramer, W. (2013). Spatial decoupling of agricultural production and consumption: Quantifying dependences of countries on food imports due to domestic land and water constraints. Environmental Research Letters, 8(1), 014046.
https://iopscience.iop.org/1748-9326/8/1/014046/pdf/1748-9326_8_1_014046.pdf
Ghahroodi, E. M., Noory, H., & Liaghat, A. M. (2015). Performance evaluation study and hydrologic and productive analysis of irrigation systems at the Qazvin irrigation network (Iran). Agricultural Water Management, 148, 189–195.
https://doi.org/10.1016/j.agwat.2014.10.003
Giaffreda, R., Antonelli, F., & Spada, P. (2019). Promoting sustainable agricultural practices through incentives. In 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 1–6. IEEE.
https://ieeexplore.ieee.org/abstract/document/8909281/
Hammouch, H., El-Yacoubi, M., Qin, H., & Berbia, H. (2024). A systematic review and meta-analysis of intelligent irrigation systems. IEEE Access, 12, 10577970.
https://ieeexplore.ieee.org/iel8/6287639/10380310/10577970.pdf
Jaiswal, N., Kumar, T. V., & Shukla, C. (2025). Smart drip irrigation systems using IoT: a review of architectures, machine learning models, and emerging trends. Discover Agriculture, 3, 253.
https://link.springer.com/article/10.1007/s44279-025-00430-1
Jena, S. P., & Chakravarty, S. (2024). Internet of things-based remote monitoring and classification of Spinacia oleracea leaf disease using deep learning approach. International Journal of Web and Grid Services, 20(2), 138597.
https://www.inderscienceonline.com/doi/abs/10.1504/IJWGS.2024.138597
Kashyap, P. K., Kumar, S., Jaiswal, A., Prasad, M., & Gandomi, A. H. (2021). Towards precision agriculture: IoT‑enabled intelligent irrigation systems using deep learning neural network. IEEE Sensors Journal, 21(16), 17479–17491.
https://ieeexplore.ieee.org/abstract/document/9388691/
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Tsouros, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
https://www.mdpi.com/1424-8220/18/8/2674
Martelli, A., Rapinesi, D., Verdi, L., Donati, I. I. M., Dalla Marta, A., & Altobelli, F. (2025). Smart irrigation for management of processing tomato: a machine learning approach. Irrigation Science, 43(11), 1407–1424.
https://link.springer.com/article/10.1007/s00271-024-00993-9
Mehedi, I. M., Hanif, M. S., Bilal, M., & Vellingiri, M. T. (2024). Remote sensing and decision support system applications in precision agriculture: Challenges and possibilities. IEEE Access, 12, 10477982.
https://ieeexplore.ieee.org/abstract/document/10477982/
Menezes, S. M., da Silva, G. F., & da Silva, M. M. (2024). Pulse drip irrigation improves yield, physiological responses, and water-use efficiency of sugarcane. Environmental and Sustainability Conservation Science.
https://link.springer.com/article/10.1007/s41101-024-00258-8
Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies. Sensors, 25(12).
http://www.turjaf.com/index.php/TURSTEP/article/view/551
Mohyuddin, G., Khan, M. A., Haseeb, A., & Mahpara, S. (2024). Evaluation of machine learning approaches for precision farming in smart agriculture system: a comprehensive review. IEEE Access, 12, 10504121.
https://ieeexplore.ieee.org/abstract/document/10504121/
Mousavi, S. M., & Khademzadeh, A. (2022). The role of low‑power wide‑area network technologies in Internet of Things: A systematic and comprehensive review. Digital Communications and Networks, 8(4), 503–522.
https://doi.org/10.1002/dac.5036
Oğuztürk, G. E. (2025). AI‑driven irrigation systems for sustainable water management: A systematic review and meta‑analytical insights. Smart Agricultural Technology, 11, 100982.
https://www.sciencedirect.com/science/article/pii/S2772375525002151
Pereira, L. S., Cordery, I., & Iacovides, I. (2012). Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agricultural Water Management, 108, 39–51.
https://ideas.repec.org/a/eee/agiwat/v108y2012icp39-51.html
Saikai, Y., Peake, A., & Chenu, K. (2023). Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback. PLOS Water, 2(9), e0000169.
https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000169
Seyedzadeh, A., & Khazaee, P. (2022). Irrigation management evaluation of multiple irrigation methods using performance indicators. ISH Journal of Hydraulic Engineering, 28(4), 345–356.
https://www.tandfonline.com/doi/abs/10.1080/09715010.2021.1891470
Singh, A., Kumar, R., & Sharma, P. (2025). A comprehensive review of recent advances in intelligent controller development for smart irrigation systems. Discover Computing, 28, Article 239.
https://link.springer.com/article/10.1007/s10791-025-09762-4
Sui, R. (2017). Irrigation scheduling using soil moisture sensors. Journal of Agricultural Science, 10(1), 1–11. U.S. Department of Agriculture.
https://www.ars.usda.gov/ARSUserFiles/60663500/Publications/Sui/2018/Sui_2018_JAS_10-1-1-11.pdf
Tace, Y., Elfilali, S., Tabaa, M., & Leghris, C. (2023). Implementation of smart irrigation using IoT and artificial intelligence. Mathematical Modeling and Computing, 10(2), 575–589.
https://doi.org/10.23939/mmc2023.02.575
Togneri, R., Dos Santos, D. F., Camponogara, G., Nagano, H., Custodio, G., Prati, R., & Fernandes, S. (2022). Soil moisture forecast for smart irrigation: The primetime for machine learning. Expert Systems with Applications, 208, 118229.
https://www.sciencedirect.com/science/article/pii/S0957417422009563
Touil, S., Richa, A., & Fizir, M. (2022). A review on smart irrigation management strategies and their effect on water savings and crop yield. Irrigation and Drainage, 71(4), 1234–1250.
https://onlinelibrary.wiley.com/doi/abs/10.1002/ird.2735
Vallejo‑Gómez, D., Osorio, M., & Hincapié, C. A. (2023). Smart irrigation systems in agriculture: A systematic review. Agronomy, 13(2), 342.
https://www.mdpi.com/2073-4395/13/2/342
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80.
https://www.sciencedirect.com/science/article/pii/S0308521X16303754
Zhao, H., Di, L., Guo, L., Zhang, C., & Lin, L. (2023). An automated data-driven irrigation scheduling approach using model simulated soil moisture and evapotranspiration. Sustainability, 15(17), 12908.
https://www.mdpi.com/2071-1050/15/17/12908
