Artificial Intelligence, Deep Learning, and Internet of Things Applications in Agricultural Smart Irrigation Systems

Year 2024, Volume: 20 Issue: 1, 41 - 60, 30.04.2024

Hasan Şahin

Abstract

Water management in agricultural irrigation is undoubtedly one of the most important topics. Considering the current issues such as climate change, global warming, and the water crisis, water supply for agricultural irrigation purposes is expected to emerge as a much more important problem in the future. Therefore, water loss should be minimized by optimizing water use in agricultural irrigation. Recently, with these concerns, Artificial Intelligence (AI) management, Deep Learning (DL) techniques, and Internet of Things (IoT) applications have been utilized in agricultural irrigation. Smart irrigation systems can also be recommended for medium-scale farmers however the efficiency of the system depends on different parameters, such as the size of the irrigated agricultural area, land topography, product type, water source, and environmental factors. The use of smart irrigation systems for large-scale agricultural areas is becoming more necessary due to the decrease in water resources. To irrigate large agricultural areas effectively, accurately, and optimally, it is recommended to use a system consisting of different sensors, satellite images, weather forecast data, and automatic control systems. However, while promoting the use of smart irrigation systems and other new agricultural technologies, it is important issue that not to overlook the need to inform farmers by relevant institutions to prevent them from investing in the wrong technologies. Instead of importing new agricultural technologies, promoting domestic production and ensuring coordination among public institutions should be facilitated.

Keywords

Smart Irrigation, Water Supply, Artificial Intelligence, Agriculture

Tarımsal Akıllı Sulama Sistemlerinde Yapay Zekâ, Derin Öğrenme ve Nesnelerin İnterneti Uygulamaları

Abstract

Tarımsal sulamada su yönetimi, şüphesiz en önemli başlıklardan birisidir. Tarımsal sulama amaçlı su tedarikinin, gündemde olan iklim değişikliği, küresel ısınma ve su krizi gibi hususlar da göz önüne alındığında, ileriki zamanlarda çok daha önemli bir sorun olarak karşımıza çıkacağı tahmin edilmektedir. Bu yüzden, tarımsal sulamada su kullanımının optimizasyonu ile su kaybının en aza indirilmesi gerekmektedir. Son zamanlarda, bu endişelerle, tarımsal sulamada yapay zekâ (AI) yönetimi, derin öğrenme (DL) teknikleri ve nesnelerin interneti (IoT) uygulamalarından faydalanılmaktadır. Akıllı sulama sistemleri orta ölçekli çiftçiler için de önerilebilmektedir ancak sistemin verimliliği; sulanan tarım alanının büyüklüğü, arazi topoğrafyası, ürün çeşidi, su kaynağı, çevresel faktörler gibi farklı parametrelere bağlıdır. Büyük ölçekli tarım alanları için akıllı sulama sistemlerinin kullanımı, su kaynaklarının azalmasından dolayı daha da zorunlu hale gelmektedir. Büyük ölçekli tarımsal alanların etkili, doğru ve optimum bir şekilde sulanabilmesi için farklı sensörler, uydu görüntüleri, hava tahmin değerleri ve otomatik kontrol elemanlarından oluşan sistemlerin kullanımı önerilmektedir. Ancak, akıllı sulama sistemleri ve diğer yeni tarım teknolojilerinin kullanımı özendirilirken, çiftçilerin de ilgili kurumlar tarafından bilgilendirilerek yanlış teknolojilere yatırım yapmalarının önlenmesi konusu unutulmaması gereken önemli bir husustur. Yeni tarım teknolojilerinde ithalat yerine, yerli üretimin teşvik edilmesi ve kamu kurumlarının koordinasyonu sağlanmalıdır.

Keywords

Akıllı Sulama, Su Temini, Yapay Zeka, Tarım

References

• Abdelmoamen Ahmed, A., Al Omari, S., Awal, R., Fares, A., ve Chouikha, M. (2021). A distributed system for supporting smart irrigation using Internet of Things technology. Engineering Reports, 3(7), e12352. https://doi.org/10.1002/eng2.12352
• Abdulla, M., ve Marhoon, A. (2023). Deep learning and IoT for monitoring tomato plant. Iraqi Journal for Electrical and Electronic Engineering, 19(1), 70-78. https://doi.org/10.37917/ijeee.19.1.9
• Abernethy, C. L. (2010). Governance of irrigation systems: Does history offer lessons for today? Irrigation and Drainage, 59(1), 31-39. https://doi.org/10.1002/ird.552
• Abioye, E. A., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Ishak, M. H. I., Rahman, M. K. I. A., Otuoze, A. O., Onotu, P., ve Ramli, M. S. A. (2020). A review on monitoring and advanced control strategies for precision irrigation. Computers and Electronics in Agriculture, 173, 105441. https://doi.org/10.1016/j.compag.2020.105441
• Ahansal, Y., Bouziani, M., Yaagoubi, R., Sebari, I., Sebari, K., ve Kenny, L. (2022). Towards smart irrigation: A literature review on the use of geospatial technologies and machine learning in the management of water resources in arboriculture. Agronomy, 12(2), 297. https://doi.org/10.3390/agronomy12020297
• Ahmad, S., Kalra, A., ve Stephen, H. (2010). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69-80. https://doi.org/10.1016/j.advwatres.2009.10.008
• Alves, R. G., Maia, R. F., ve Lima, F. (2023). Development of a Digital Twin for smart farming: Irrigation management system for water saving. Journal of Cleaner Production, 388, 135920. https://doi.org/10.1016/j.jclepro.2023.135920
• AlZu’bi, S., Hawashin, B., Mujahed, M., Jararweh, Y., ve Gupta, B. B. (2019). An efficient employment of internet of multimedia things in smart and future agriculture. Multimedia Tools and Applications, 78(20), 29581-29605. https://doi.org/10.1007/s11042-019-7367-0
• Arias, M., Notarnicola, C., Campo-Bescós, M. Á., Arregui, L. M., ve Álvarez-Mozos, J. (2023). Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields. Agricultural Water Management, 287, 108422. https://doi.org/10.1016/j.agwat.2023.108422
• Aubriot, O. (2022). The history and politics of communal irrigation: A Review. Water alternatives, 15(2), 307-340.
• Bhardwaj, A., Kumar, M., Alshehri, M., Keshta, I., Abugabah, A., ve Sharma, S. K. (2022). Smart water management framework for irrigation in agriculture. Environmental Technology, 45(12), 2320-2334. https://doi.org/10.1080/09593330.2022.2039783
• Bhatti, E. U. H., Khan, M. M., Shah, S. A. R., Raza, S. S., Shoaib, M., ve Adnan, M. (2019). Dynamics of water quality: Impact assessment process for water resource management. Processes, 7(2), 102. https://doi.org/10.3390/pr7020102
• Bjornlund, H., van Rooyen, A., Pittock, J., Parry, K., Moyo, M., Mdemu, M., ve de Sousa, W. (2020). Institutional innovation and smart water management technologies in small-scale irrigation schemes in southern Africa. Water International, 45(6), 621-650. https://doi.org/10.1080/02508060.2020.1804715
• Bojago, E., ve Abrham, Y. (2023). Small-scale irrigation (SSI) farming as a climate-smart agriculture (CSA) practice and its influence on livelihood improvement in Offa District, Southern Ethiopia. Journal of Agriculture and Food Research, 12, 100534. https://doi.org/10.1016/j.jafr.2023.100534
• Bouali, E. T., Abid, M. R., Boufounas, E. M., Hamed, T. A., ve Benhaddou, D. (2022). Renewable energy integration into cloud IoT-based smart agriculture. IEEE Access, 10, 1-17. https://doi.org/10.1109/ACCESS.2021.3138160
• Cardenas-Lailhacar, B., Dukes, M. D., ve Miller, G. L. (2008). Sensor-based automation of irrigation on bermudagrass, during wet weather conditions. Journal of Irrigation and Drainage Engineering, 134(2), 120-128. https://doi.org/10.1061/(asce)0733-9437(2008)134:2(120)
• Cardenas-Lailhacar, B., Dukes, M. D., ve Miller, G. L. (2010). Sensor-based automation of irrigation on bermudagrass during dry weather conditions. Journal of Irrigation and Drainage Engineering, 136(3), 184-193. https://doi.org/10.1061/(asce)ir.1943-4774.0000153
• Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q., ve Cai, K. (2020). A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management, 240, 106303. https://doi.org/10.1016/j.agwat.2020.106303
• Dyantyi, O., ve Njenga, J. (2022). Awareness and perceptions of smart ırrigation technologies by small scale farmers in Rural South Africa. 2022 IST-Africa Conference, IST-Africa 2022. https://doi.org/10.23919/IST-Africa56635.2022.9845613
• FAO, ve FAO http://www.fao.org/nr/water. (2018). CROPWAT 8.0. Land and Water, Databases and Software, CropWat.
• Fathy, C., ve Ali, H. M. (2023). A secure IoT-based irrigation system for precision agriculture using the expeditious cipher. Sensors, 23(4), 2091. https://doi.org/10.3390/s23042091
• Fischer, M., Heim, D., Hofmann, A., Janiesch, C., Klima, C., ve Winkelmann, A. (2020). A taxonomy and archetypes of smart services for smart living. Electronic Markets, 30(1), 131-149. https://doi.org/10.1007/s12525-019-00384-5
• Gbodji, K. K., Quarmine, W., ve Minh, T. T. (2023). Effective demand for climate-smart adaptation: A case of solar technologies for cocoa irrigation in Ghana. Sustainable Environment, 9(1), 2258472. https://doi.org/10.1080/27658511.2023.2258472
• Goodfellow, I., Bengio, Y., ve Courville, A. (2016). Deep learning. The MIT Press, ISBN: 9780262337373. https://mitpress.mit.edu/9780262035613/deep-learning/.
• Hachimi, C. El, Belaqziz, S., Khabba, S., Sebbar, B., Dhiba, D., ve Chehbouni, A. (2023). Smart weather data management based on artificial intelligence and big data analytics for precision agriculture. Agriculture, 13(1), 95. https://doi.org/10.3390/agriculture13010095
• Hadidi, A., Saba, D., ve Sahli, Y. (2022). Smart irrigation system for smart agricultural using IoT: Concepts, architecture, and applications. R. Bhatnagar, N. K. Tripathi, N. Bhatnagar, ve C. K. Panda (Editörler), The Digital Agricultural Revolution: Innovations and Challenges in Agriculture through Technology Disruptions. Wiley. https://doi.org/10.1002/9781119823469.ch7
• Haziq, M., Pang, W. L., Chan, K. Y., Lee, I. E., Chung, G. C., ve Wong, S. K. (2022). High-efficiency low-cost smart IoT agriculture irrigation, soil’s fertility and moisture controlling system. Universal Journal of Agricultural Research, 10(6), 785-793. https://doi.org/10.13189/ujar.2022.100616
• Ilyas, A., Parkinson, S., Vinca, A., Byers, E., Manzoor, T., Riahi, K., Willaarts, B., Siddiqi, A., ve Muhammad, A. (2022). Balancing smart irrigation and hydropower investments for sustainable water conservation in the Indus basin. Environmental Science and Policy, 135, 147-161. https://doi.org/10.1016/j.envsci.2022.04.012
• Jain, R. K. (2023). Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications. Smart Agricultural Technology, 4, 100215. https://doi.org/10.1016/j.atech.2023.100215
• Jiménez, A. F., Cárdenas, P. F., ve Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635. https://doi.org/10.1016/j.compag.2021.106635
• Kaur, K., Mahajan, R., Bagai, D., ve Student, M. E. (2007). A review of various soil moisture measurement techniques. International Journal of Innovative Research in Science, Engineering and Technology, 5(4), 5774-5778.
• Kavyashree T, ve Shreedhara KS. (2021). Intelligent IoT based smart irrigation system. International Journal of Creative Research Thoughts, 9(2), 2709-2722.
• Khachatryan, H., Rihn, A., Suh, D. H., ve Dukes, M. (2020). Homeowners’ preferences for smart irrigation systems and features. EDIS, 2020(5), FE1080. https://doi.org/10.32473/edis-fe1080-2020
• Khachatryan, H., Suh, D. H., Xu, W., Useche, P., ve Dukes, M. D. (2019). Towards sustainable water management: Preferences and willingness to pay for smart landscape irrigation technologies. Land Use Policy, 85, 33-41. https://doi.org/10.1016/j.landusepol.2019.03.014
• Khashiboun, K., Zilberman, A., Shaviv, A., Starosvetsky, J., ve Armon, R. (2007). The fate of Cryptosporidium parvum oocysts in reclaimed water irrigation-history and non-history soils irrigated with various effluent qualities. Water, Air, and Soil Pollution, 185, 33-41. https://doi.org/10.1007/s11270-007-9420-2
• Khriji, S., El Houssaini, D., Kammoun, I., ve Kanoun, O. (2021). Precision irrigation: An IoT-enabled Wireless Sensor Network for smart irrigation systems. S. Khriji, D. El Houssaini, I. Kammoun, ve O. Kanoun (Editörler). Women in Precision Agriculture. Springer. https://doi.org/10.1007/978-3-030-49244-1_6
• Krishnan, R. S., Julie, E. G., Robinson, Y. H., Raja, S., Kumar, R., Thong, P. H., ve Son, L. H. (2020). Fuzzy Logic based smart irrigation system using Internet of Things. Journal of Cleaner Production, 252, 119902. https://doi.org/10.1016/j.jclepro.2019.119902
• Kurtulmuş, E., Arslan, B., ve Kurtulmuş, F. (2022). Deep learning for proximal soil sensor development towards smart irrigation. Expert Systems with Applications, 198, 116812. https://doi.org/10.1016/j.eswa.2022.116812
• Lakshmiprabha, K. E., ve Govindaraju, C. (2023). Hydroponic-based smart irrigation system using Internet of Things. International Journal of Communication Systems, 36(12), e4071. https://doi.org/10.1002/dac.4071
• Li, X., Wang, Y., Hu, Y., Zhou, C., ve Zhang, H. (2022). Numerical ınvestigation on stratum and surface deformation in underground phosphorite mining under different mining methods. Frontiers in Earth Science, 10, 831856. https://doi.org/10.3389/feart.2022.831856
• Masseroni, D., Arbat, G., ve de Lima, I. P. (2020). Editorial-managing and planning water resources for irrigation: Smart-irrigation systems for providing sustainable agriculture and maintaining ecosystem services. Water, 12(1), 263. https://doi.org/10.3390/w12010263
• Mateo-Sanchis, A., Piles, M., Amorós-López, J., Muñoz-Marí, J., Adsuara, J. E., Moreno-Martínez, Á., ve Camps-Valls, G. (2021). Learning main drivers of crop progress and failure in Europe with interpretable machine learning. International Journal of Applied Earth Observation and Geoinformation, 104, 102574. https://doi.org/10.1016/j.jag.2021.102574
• Murgabayev, S. S., Maldybekova, L. D., Bakhtybaev, M. M., Zhetybaev, K. M., Gursoy, M., ve Sizdikov, B. S. (2022). History of the syganak irrigation. Povolzhskaya Arkheologiya, 2(40), 206-214. https://doi.org/10.24852/PA2022.2.40.206.214
• Muthuminal, R., ve Priya, R. M. (2023). An outlook over smart irrigation system for sustainable rural development. R. Muthuminal, ve R. M. Priya (Editörler). Smart village infrastructure and sustainable rural communities. IGI Global. https://doi.org/10.4018/978-1-6684-6418-2.ch008
• Ndunagu, J. N., Ukhurebor, K. E., Akaaza, M., ve Onyancha, R. B. (2022). Development of a wireless sensor network and IoT-based smart irrigation system. Applied and Environmental Soil Science, 2022, 7678570. https://doi.org/10.1155/2022/7678570
• Olatunji, K. A. , Oguntimilehin A. ve Adeyemo O. A (2020). A mobile phone controllable smart irrigation system. International Journal of Advanced Trends in Computer Science and Engineering, 9(1), 279-284. https://doi.org/10.30534/ijatcse/2020/42912020
• Otavio, N. A. S., Marcos, V. F., Bruno, P. L., Jefferson, V. J., Eder, D. F. J., Joao, P. F., Irineu, P. de S. A., ve Renata, A. S. (2016). Irrigation history and pruning effect on growth and yield of jatropha on a plantation in southeastern Brazil. African Journal of Agricultural Research, 11(50), 5080-5091. https://doi.org/10.5897/ajar2016.11696
• Perez-Blanco, C. D., Hrast-Essenfelder, A., ve Perry, C. (2020). Irrigation technology and water conservation: A review of the theory and evidence. Review of Environmental Economics and Policy, 14(2), 216-239. https://doi.org/10.1093/REEP/REAA004
• Phasinam, K., Kassanuk, T., Shinde, P. P., Thakar, C. M., Sharma, D. K., Mohiddin, M. K., ve Rahmani, A. W. (2022). Application of IoT and Cloud Computing in automation of agriculture irrigation. Journal of Food Quality, 2022, 8285969. https://doi.org/10.1155/2022/8285969
• Raffelli, G., Previati, M., Canone, D., Gisolo, D., Bevilacqua, I., Capello, G., Biddoccu, M., Cavallo, E., Deiana, R., Cassiani, G., ve Ferraris, S. (2017). Local- and plot-scale measurements of soil moisture: Time and spatially resolved field techniques in plain, hill and mountain sites. Water, 9(9), 706. https://doi.org/10.3390/w9090706
• Ransbotham, S., Kiron, D., Gerbert, P., ve Reeves, M. (2017). Reshaping business with Artificial Intelligence: Closing the gap between ambition and action. MIT Sloan Management Review. 59(1), 59181.
• Rasheed, M. W., Tang, J., Sarwar, A., Shah, S., Saddique, N., Khan, M. U., Imran Khan, M., Nawaz, S., Shamshiri, R. R., Aziz, M., ve Sultan, M. (2022). Soil moisture measuring techniques and factors affecting the moisture dynamics: A comprehensive review. Sustainability, 14(18), 11538. https://doi.org/10.3390/su141811538
• Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., ve Pablo, J. D. E. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117. https://doi.org/10.3846/jbem.2020.13641
• Şahin, H. (2022). Digital Agriculture , Agriculture 4 . 0 , Intelligent Agriculture , Robotic Applications and autonomous. Tarım Makinaları Bilimi Dergisi 18, 68–83.
• Sami, M., Khan, S. Q., Khurram, M., Farooq, M. U., Anjum, R., Aziz, S., Qureshi, R., ve Sadak, F. (2022). A deep learning-based sensor modeling for smart irrigation system. Agronomy, 12(1), 212. https://doi.org/10.3390/agronomy12010212
• Sasi Kumar, G., Nagaraju, G., Rohith, D., ve Vasudevarao, A. (2023). Design and development of smart ırrigation system using Internet of Things (IoT) - A case study. Nature Environment and Pollution Technology, 22(1), 523-526. https://doi.org/10.46488/NEPT.2023.v22i01.052
• Serote, B., Mokgehle, S., Plooy, C. Du, Mpandeli, S., Nhamo, L., ve Senyolo, G. (2021). Factors influencing the adoption of climate-smart irrigation technologies for sustainable crop productivity by smallholder farmers in arid areas of South Africa. Agriculture, 11(12), 1222. https://doi.org/10.3390/agriculture11121222
• Sharma, A. K., Hubert-Moy, L., Buvaneshwari, S., Sekhar, M., Ruiz, L., Bandyopadhyay, S., ve Corgne, S. (2018). Irrigation history estimation using multitemporal landsat satellite images: Application to an intensive groundwater irrigated agricultural watershed in India. Remote Sensing, 10(6), 893. https://doi.org/10.3390/rs10060893
• Shrestha, Y. R., Krishna, V., ve von Krogh, G. (2021). Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123, 588-603. https://doi.org/10.1016/j.jbusres.2020.09.068
• Singh, A. K., Bhardwaj, A. K., Verma, C. L., ve Mishra, V. K. (2019). Soil moisture sensing techniques for scheduling irrigation. Journal of Soil Salinity and Water Quality, 11(1), 68-76.
• Srivastava, P. K., Petropoulos, G. P., ve Kerr, Y. H. (2016). Satellite soil moisture retrieval: Techniques and applications. P. K. Srivastava (Editör). Satellite soil moisture retrieval: Techniques and applications. Elsevier.
• Stolojescu-Crisan, C., Butunoi, B. P., ve Crisan, C. (2022). An IoT based smart irrigation system. IEEE Consumer Electronics Magazine, 11(3), 50-58. https://doi.org/10.1109/MCE.2021.3084123
• Suh, D. H., Khachatryan, H., Rihn, A., ve Dukes, M. (2017). Relating knowledge and perceptions of sustainable water management to preferences for smart irrigation technology. Sustainability, 9(4), 607. https://doi.org/10.3390/su9040607
• Suresh, P., Aswathy, R. H., Arumugam, S., Albraikan, A. A., Al-Wesabi, F. N., Hilal, A. M., ve Alamgeer, M. (2022). IoT with evolutionary algorithm based deep learning for smart irrigation system. Computers, Materials and Continua, 71(1), 1713-1728. https://doi.org/10.32604/cmc.2022.021789
• Susha Lekshmi, S. U., Singh, D. N., ve Shojaei Baghini, M. (2014). A critical review of soil moisture measurement. In Measurement: Journal of the International Measurement, 54, 92-105. https://doi.org/10.1016/j.measurement.2014.04.007
• Ugli, A. M. I. (2022). History of irrigation in the Fergana Valley. International Journal for Research in Applied Science and Engineering Technology, 10(4), 157-159. https://doi.org/10.22214/ijraset.2022.40960
• UN. (2020). Goal 11: Make cities inclusive, safe, resilient and sustainable. United Nations.
• Vallejo-Gómez, D., Osorio, M., ve Hincapié, C. A. (2023). Smart irrigation systems in agriculture: A systematic review. Agronomy, 13(2), 342. https://doi.org/10.3390/agronomy13020342
• Vij, A., Vijendra, S., Jain, A., Bajaj, S., Bassi, A., ve Sharma, A. (2020). IoT and Machine Learning Approaches for Automation of Farm Irrigation System. Procedia Computer Science, 167, 1250-1257. https://doi.org/10.1016/j.procs.2020.03.440
• Walker, J. P., Willgoose, G. R., ve Kalma, J. D. (2004). In situ measurement of soil moisture: A comparison of techniques. Journal of Hydrology, 293(1–4), 85-99. https://doi.org/10.1016/j.jhydrol.2004.01.008
• Wang, Z., Li, M., Lu, J., ve Cheng, X. (2022). Business innovation based on artificial intelligence and Blockchain technology. Information Processing and Management, 59(1), 102759. https://doi.org/10.1016/j.ipm.2021.102759
• Wang A, Y., G., Hu, P., Lai, X., Xue, B., ve Fang, Q. (2022). Root-zone soil moisture estimation based on remote sensing data and deep learning. Environmental Research, 212, 113178. https://doi.org/10.1016/j.envres.2022.113278
• Wu, X., Walker, J. P., Jonard, F., ve Ye, N. (2022). Inter-comparison of proximal near-surface soil moisture measurement techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2370-2378. https://doi.org/10.1109/JSTARS.2022.3156878
• Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L. F., ve Nie, W. (2021). A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin County, China. ISPRS International Journal of Geo-Information, 10(2), 93. https://doi.org/10.3390/ijgi10020093
• Yonbawi, S., Alahmari, S., Raju, B. R. S. S., Rao, C. H. G., Ishak, M. K., Alkahtani, H. K., Varela-Aldás, J., ve Mostafa, S. M. (2023). Modeling of sensor enabled irrigation management for Intelligent Agriculture using Hybrid Deep Belief Network. Computer Systems Science and Engineering, 46(2), 2319-2335. https://doi.org/10.32604/csse.2023.036721
• Zeng, W., Ao, C., ve Lei, G. (2023). History of irrigation in China: Schedule and Method Development. S. Eslamian, ve F. Eslamian (Editörler). Handbook of Irrigation Hydrology and Management: Irrigation Case Studies. CRC Press. https://doi.org/10.1201/9781003353928-12
• Zhang, X., ve Khachatryan, H. (2019). Investigating homeowners’ preferences for smart irrigation technology features. Water, 11(10), 1996. https://doi.org/10.3390/w11101996