Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-35224 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 20 article 973 pages: 657-672

Irfan Ardiansah*
Department of Agro-Industrial Technology, Faculty of Agro-Industrial Technology, UniversitasPadjadjaran, Indonesia

Nurpilihan Bafdal
Department of Agriculture Engineering and Biosystem, Faculty of Agro-Industrial Technology, UniversitasPadjadjaran, Indonesia

Awang Bono
Department of Chemical Engineering, Faculty of Engineering, Universiti Malaysia Sabah, Malaysia

Edy Suryadi
Department of Agriculture Engineering and Biosystem, Faculty of Agro-Industrial Technology, UniversitasPadjadjaran, Indonesia

Siti Nurhasanah
Department of Food-Industrial Technology, Faculty of Agro-Industrial Technology, UniversitasPadjadjaran, Indonesia

Food security is an issue that arises as a result of the rising population since population growth decreases agricultural land, leading to water scarcity. Agriculture requires large amounts of water, but water scarcity forces farmers to irrigate their crops with little or low-quality water, leading to the idea of developing smart irrigation. The challenge is how to manage the interactions between plants, growing media, microclimate, and water using manufactured systems. Good irrigation management will minimize the occurrence of poor irrigation design. This review is a way to present various methods and approaches for using sensors, controllers, the Internet of Things, and artificial intelligence in irrigation systems with a focus on improving water use efficiency. The study uses SCOPUS indexed publications and proceedings to study the evolution of irrigation information technology over the last eleven years. We hope this review can serve as a source of information to broaden the validity of the findings of irrigation monitoring and control technologies and help researchers identify future research directions on this subject.

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1. Gillespie, S., & van den Bold, M. (2017). Agriculture, Food Systems, and Nutrition: Meeting the Challenge. Global Challenges, 1(3), 1600002.

2. Ayres, R. U., van den Bergh, J. C. J. M., Lindenberger, D., & Warr, B. (2013). The underestimated contribution of energy to economic growth. Structural Change and Economic Dynamics, 27, 79–88.

3. Dagnino, M., & Ward, F. A. (2012). Economics of Agricultural Water Conservation: Empirical Analysis and Policy Implications. International Journal of Water Resources Development, 28(4), 577–600.

4. Olayide, O. E., Tetteh, I. K., & Popoola, L. (2016). Differential impacts of rainfall and irrigation on agricultural production in Nigeria: Any lessons for climate-smart agriculture? Agricultural Water Management, 178, 30–36.

5. Tierno, R., Carrasco, A., Ritter, E., & de Galarreta, J. I. R. (2014). Differential Growth Response and Minituber Production of Three Potato Cultivars Under Aeroponics and Greenhouse Bed Culture. American Journal of Potato Research, 91(4), 346–353.

6. Sambo, P., Nicoletto, C., Giro, A., Pii, Y., Valentinuzzi, F., Mimmo, T., … Cesco, S. (2019). Hydroponic Solutions for Soilless Production Systems: Issues and Opportunities in a Smart Agriculture Perspective. Frontiers in Plant Science. Retrieved from

7. Sisodia, G. S., Alshamsi, R., & Sergi, B. S. (2021). Business valuation strategy for new hydroponic farm development – a proposal towards sustainable agriculture development in United Arab Emirates. British Food Journal, 123(4), 1560–1577.

8. Vadiee, A., & Martin, V. (2014). Energy management strategies for commercial greenhouses. Applied Energy, 114, 880–888.

9. Liu, H., Li, H., Ning, H., Zhang, X., Li, S., Pang, J., … Sun, J. (2019). Optimizing irrigation frequency and amount to balance yield, fruit quality and water use efficiency of greenhouse tomato. Agricultural Water Management, 226, 105787.

10. Chen, J., Kang, S., Du, T., Qiu, R., Guo, P., & Chen, R. (2013). Quantitative response of greenhouse tomato yield and quality to water deficit at different growth stages. Agricultural Water Management, 129, 152–162.

11. Saccon, P. (2018). Water for agriculture, irrigation management. Applied Soil Ecology, 123(October), 793–796.

12. Bafdal, N., & Dwiratna, S. (2018). Water harvesting system as an alternative appropriate technology to supply irrigation on red oval cherry tomato production. International Journal on Advanced Science, Engineering and Information Technology, 8(2), 561–566.

13. Pasika, S., & Gandla, S. T. (2020). Smart water quality monitoring system with cost-effective using IoT. Heliyon, 6(7), e04096.

14. Kamienski, C., Soininen, J. P., Taumberger, M., Dantas, R., Toscano, A., Cinotti, T. S., … Neto, A. T. (2019). Smart water management platform: IoT-based precision irrigation for agriculture. Sensors (Switzerland), 19(2).

15. Matyakubov, B., Begmatov, I., Raimova, I., & Teplova, G. (2020). Factors for the efficient use of water distribution facilities. IOP Conference Series: Materials Science and Engineering, 883(1).

16. Nawandar, N. K., & Satpute, V. R. (2019). IoT based low cost and intelligent module for smart irrigation system. Computers and Electronics in Agriculture, 162(May), 979–990.

17. Ardiansah, I., Bafdal, N., Bono, A., Suryadi, E., & Husnuzhan, R. (2021). Impact Of Ventilations In Electronic Device Shield On Micro-climate Data Acquired In A Tropical Greenhouse. INMATEH - Agricultural Engineering, 63(1), 397–404.

18. Angelopoulos, C. M., Filios, G., Nikoletseas, S., & Raptis, T. P. (2020). Keeping data at the edge of smart irrigation networks: A case study in strawberry greenhouses. Computer Networks, 167, 107039.

19. Edet, U., & Mann, D. (2020). Visual information requirements for remotely supervised autonomous agricultural machines. Applied Sciences (Switzerland), 10(8).

20. Vera, J., Conejero, W., Mira-García, A. B., Conesa, M. R., & Ruiz-Sánchez, M. C. (2021). Towards irrigation automation based on dielectric soil sensors. Journal of Horticultural Science and Biotechnology, 00(00), 1–12.

21. Yuan, Z., Olsson, G., Cardell-Oliver, R., van Schagen, K., Marchi, A., Deletic, A., … Jiang, G. (2019). Sweating the assets – The role of instrumentation, control and automation in urban water systems. Water Research, 155, 381–402.

22. Nageswara Rao, R., & Sridhar, B. (2018). IoT based smart crop-field monitoring and automation irrigation system. Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, (Icisc), 478–483.

23. Uddin, J., Smith, R. J., Gillies, M. H., Moller, P., & Robson, D. (2018). Smart Automated Furrow Irrigation of Cotton. Journal of Irrigation and Drainage Engineering, 144(5), 04018005.

24. Taneja, K., & Bhatia, S. (2017). Automatic irrigation system using Arduino UNO. Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017, 2018-Janua, 132–135.

25. Millán, S., Casadesús, J., Campillo, C., Moñino, M. J., & Prieto, M. H. (2019). Using soil moisture sensors for automated irrigation scheduling in a plum crop. Water (Switzerland), 11(10), 1–18.

26. Karasekreter, N., Başçiftçi, F., & Fidan, U. (2013). A new suggestion for an irrigation schedule with an artificial neural network. Journal of Experimental and Theoretical Artificial Intelligence, 25(1), 93–104.

27. Ferrarezi, R. S., Dove, S. K., & Van Iersel, M. W. (2015). An automated system for monitoring soil moisture and controlling irrigation using low-cost open-source microcontrollers. HortTechnology, 25(1), 110–118.

28. Almarshadi, M. H., & Ismail, S. M. (2011). Effects of precision irrigation on productivity and water use efficiency of Alfalfa under different irrigation methods in arid climates. Journal of Applied Sciences Research, 7(3), 299–308.

29. Kumar Sahu, C., & Behera, P. (2015). A low cost smart irrigation control system. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS) (pp. 1146–1152). IEEE.

30. Ardiansah, I., Bafdal, N., Suryadi, E., & Bono, A. (2021). Design of micro-climate data monitoring system for tropical greenhouse based on arduino UNO and raspberry pi. IOP Conference Series: Earth and Environmental Science, 757(1).

31. Stambouli, T., Faci, J. M., & Zapata, N. (2014). Water and energy management in an automated irrigation district. Agricultural Water Management, 142, 66–76.

32. Mason, B., Rufí-Salís, M., Parada, F., Gabarrell, X., & Gruden, C. (2019). Intelligent urban irrigation systems: Saving water and maintaining crop yields. Agricultural Water Management, 226(September), 105812.

33. Munir, M. S., Bajwa, I. S., Naeem, M. A., & Ramzan, B. (2018). Design and implementation of an IoT system for smart energy consumption and smart irrigation in tunnel farming. Energies, 11(12).

34. Castrignanò, A., Buttafuoco, G., Khosla, R., Mouazen, A. M., Moshou, D., & Naud, O. (2020). Agricultural internet of things and decision support for precision smart farming.

35. Bafdal, N., & Dwiratna, S. (2018). Water Harvesting System As An Alternative Appropriate Technology To Supply Irrigation On Red Oval Cherry Tomato Production. International Journal on Advanced Science, Engineering and Information Technology, 8(2), 561–566.

36. Banerjee, A., Mitra, A., & Biswas, A. (2021). An Integrated Application of IoT‐Based WSN in the Field of Indian Agriculture System Using Hybrid Optimization Technique and Machine Learning. Agricultural Informatics, 171–187.

37. Finkel, H. J. (2019). Handbook of Irrigation Technology: Volume 1. CRC press.

38. Pfeiffer, L., & Lin, C. Y. C. (2014). Does efficient irrigation technology lead to reduced groundwater extraction? Empirical evidence. Journal of Environmental Economics and Management, 67(2), 189–208.

39. Wang, F., & Feng, P. (2015). Design of Intelligent Irrigation Monitoring System Based on GPRS and Zigbee. Asian Agricultural Research, 7(6), 97–100.

40. Ebrahimian, H. (2014). Soil infiltration characteristics in alternate and conventional furrow irrigation using different estimation methods. KSCE Journal of Civil Engineering, 18(6), 1904–1911.

41. Abd El-Halim, A. (2013). Impact of alternate furrow irrigation with different irrigation intervals on yield, water use efficiency, and economic return of corn. Chilean journal of agricultural research. scielocl.

42. Golzardi, F., Baghdadi, A., & Afshar, R. K. (2017). Alternate furrow irrigation affects yield and water-use efficiency of maize under deficit irrigation. Crop and Pasture Science, 68(8), 726–734. Retrieved from

43. Qiu, P., Cui, Y., Han, H., & Liu, B. (2015). Effect of flooding irrigation and intermittent irrigation patterns on weed community diversity in late rice fields. Transactions of the Chinese Society of Agricultural Engineering, 31(22).

44. Massey, J. H., Walker, T. W., Anders, M. M., Smith, M. C., & Avila, L. A. (2014). Farmer adaptation of intermittent flooding using multiple-inlet rice irrigation in Mississippi. Agricultural Water Management, 146, 297–304.

45. Chlapecka, J. L., Hardke, J. T., Roberts, T. L., Mann, M. G., & Ablao, A. (2021). Scheduling rice irrigation using soil moisture thresholds for furrow irrigation and intermittent flooding. Agronomy Journal, 113(2), 1258–1270.

46. van Iersel, M. W., Chappell, M., & Lea-Cox, J. D. (2013). Sensors for improved efficiency of irrigation in greenhouse and nursery production. HortTechnology, 23(6), 735–746.

47. Phogat, V., Mallants, D., Cox, J. W., Šimůnek, J., Oliver, D. P., & Awad, J. (2020). Management of soil salinity associated with irrigation of protected crops. Agricultural Water Management, 227(July 2019).

48. Zhang, L., Merkley, G. P., & Pinthong, K. (2013). Assessing whole-field sprinkler irrigation application uniformity. Irrigation Science, 31(2), 87–105.

49. Kandelous, M. M., Šimůnek, J., van Genuchten, M. T., & Malek, K. (2011). Soil Water Content Distributions between Two Emitters of a Subsurface Drip Irrigation System. Soil Science Society of America Journal, 75(2), 488–497.

50. Jonathan, R. C., Chavarro, J. I., Garrido, A., & Guzman, H. A. (2014). Performance evaluation of irrigation techniques through the implementation of a fuzzy logic system. ARPN Journal of Engineering and Applied Sciences, 9(7), 1087–1093.

51. Wang, J., Chen, M., Zhou, J., & Li, P. (2020). Data communication mechanism for greenhouse environment monitoring and control: An agent-based IoT system. Information Processing in Agriculture, 7(3), 444–455.

52. Tarjan, L., Šenk, I., Obúcina, J. E., Stankovski, S., & Ostojić, G. (2020). Extending legacy industrial machines by a low-cost easy-to-use iot module for data acquisition. Symmetry, 12(9).

53. Pisanu, T., Garau, S., Ortu, P., Schirru, L., & Macciò, C. (2020). Prototype of a low-cost electronic platform for real time greenhouse environment monitoring: An agriculture 4.0 perspective. Electronics (Switzerland), 9(5).

54. Zeng, Z., Zeng, F., Han, X., Elkhouchlaa, H., Yu, Q., & Lü, E. (2021). Real‐time monitoring of environmental parameters in a commercial gestating sow house using a zigbee‐based wireless sensor network. Applied Sciences (Switzerland), 11(3), 1–17.

55. Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., & Dragana, C. (2020). Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture. Sensors, 20(3), 817.

56. Quebrajo, L., Perez-Ruiz, M., Pérez-Urrestarazu, L., Martínez, G., & Egea, G. (2018). Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering, 165, 77–87.

57. Difallah, W., Benahmed, K., Bounnama, F., Draoui, B., & Saaidi, A. (2018). Intelligent irrigation management system. International Journal of Advanced Computer Science and Applications, 9(9), 429–433.

58. Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15–22.

59. Shamshiri, R. R., Bojic, I., van Henten, E., Balasundram, S. K., Dworak, V., Sultan, M., & Weltzien, C. (2020). Model-based evaluation of greenhouse microclimate using IoT-Sensor data fusion for energy efficient crop production. Journal of Cleaner Production, 263, 121303.

60. Kapse, S., & Kale, S. (2020). IOT Enable Soil Testing & NPK Nutrient Detection. Jac : a Journal of Composition Theory, XIII(V), 310–318.

61. Raza, S. E. A., Smith, H. K., Clarkson, G. J. J., Taylor, G., Thompson, A. J., Clarkson, J., & Rajpoot, N. M. (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE, 9(6), 1–10.

62. Yu, M. H., Ding, G. D., Gao, G. L., Zhao, Y. Y., Yan, L., & Sai, K. (2015). Using plant temperature to evaluate the response of stomatal conductance to soil moisture deficit. Forests, 6(10), 3748–3762.

63. Hsu, W. L., & Chang, K. T. (2019). Cross-estimation of soil moisture using thermal infrared images with different resolutions. Sensors and Materials, 31(1), 387–398.

64. Crusiol, L. G. T., Nanni, M. R., Furlanetto, R. H., Sibaldelli, R. N. R., Cezar, E., Mertz-Henning, L. M., … Farias, J. R. B. (2020). UAV-based thermal imaging in the assessment of water status of soybean plants. International Journal of Remote Sensing, 41(9), 3243–3265.

65. Laktionov, I. S., Vovna, O. V., Zori, A. A., & Lebedev, V. A. (2018). Results of simulation and physical modeling of the computerized monitoring and control system for greenhouse microclimate parameters. International Journal on Smart Sensing and Intelligent Systems, 11(0), 1–15.

66. Singh, R., Gehlot, A., Gupta, L. R., Singh, B., & Swain, M. (2019). Internet of Things with Raspberry Pi and Arduino. Internet of Things with Raspberry Pi and Arduino. CRC Press.

67. Laktionov, I., Vovna, O., & Zori, A. (2017). Copncept of low cost computerized measuring system for microclimate parameters of greenhouses. Bulgarian Journal of Agricultural Science, 23(4), 668–673.

68. Azaza, M., Tanougast, C., Fabrizio, E., & Mami, A. (2016). Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring. ISA Transactions, 61, 297–307.

69. Nikolaou, G., Neocleous, D., Katsoulas, N., & Kittas, C. (2019). Effects of Cooling Systems on Greenhouse Microclimate and Cucumber Growth under Mediterranean Climatic Conditions. Agronomy, 9(6), 300.

70. Mesas-Carrascosa, F. J., Verdú Santano, D., Meroño, J. E., Sánchez de la Orden, M., & García-Ferrer, A. (2015). Open source hardware to monitor environmental parameters in precision agriculture. Biosystems Engineering, 137, 73–83.

71. Story, D., & Kacira, M. (2015). Design and implementation of a computer vision-guided greenhouse crop diagnostics system. Machine Vision and Applications, 26(4), 495–506.

72. Abinaya, T., Ishwarya, J., & Maheswari, M. (2019). A Novel Methodology for Monitoring and Controlling of Water Quality in Aquaculture using Internet of Things (IoT). 2019 International Conference on Computer Communication and Informatics, ICCCI 2019, 1–4.

73. Najafzadeh, M., & Ghaemi, A. (2019). Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods. Environmental Monitoring and Assessment, 191(6), 380.

74. Karimi, B., Mohammadi, P., Sanikhani, H., Salih, S. Q., & Yaseen, Z. M. (2020). Modeling wetted areas of moisture bulb for drip irrigation systems: An enhanced empirical model and artificial neural network. Computers and Electronics in Agriculture, 178(September), 105767.

75. Pappu, S., Vudatha, P., Niharika, A. V., Karthick, T., & Sankaranarayanan, S. (2017). Intelligent IoT based water quality monitoring system. International Journal of Applied Engineering Research, 12(16), 5447–5454.

76. Moreira Barradas, J. M., Matula, S., & Dolezal, F. (2012). A Decision Support System-Fertigation Simulator (DSS-FS) for design and optimization of sprinkler and drip irrigation systems. Computers and Electronics in Agriculture, 86(August 2012), 111–119.

77. Khoa, T. A., Man, M. M., Nguyen, T. Y., Nguyen, V. D., & Nam, N. H. (2019). Smart agriculture using IoT multi-sensors: A novel watering management system. Journal of Sensor and Actuator Networks, 8(3).

78. Leão, T. P., da Costa, B. F. D., Bufon, V. B., & Aragón, F. F. H. (2020). Using time domain reflectometry to estimate water content of three soil orders under savanna in Brazil. Geoderma Regional, 21.

79. Yadav, D. K., Karthik, G., Jayanthu, S., & Das, S. K. (2019). Design of Real-Time Slope Monitoring System Using Time-Domain Reflectometry With Wireless Sensor Network. IEEE Sensors Letters, 3(2), 1.

80. Singh, P., & Saikia, S. (2017). Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module. IEEE Region 10 Humanitarian Technology Conference 2016, R10-HTC 2016 - Proceedings.

81. Ashifuddinmondal, M., & Rehena, Z. (2018). IoT Based Intelligent Agriculture Field Monitoring System. Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018, 625–629.

82. Muhammad F. Obead, A.Taha, I., & Salman, A. H. (2021). Design and implement of irrigation prototype system based GSM. International Journal of Computing and Digital Systems, 1–7.

83. Porselvi, T., Tresa Sangeetha, S. V, Elavarasu, R., Archana, V., Gowshni, K., & Sanmuga Piriya, T. (2021). Automatic Control And Monitoring Of Greenhouse System Using Iot. Turkish Journal of Computer and Mathematics Education, 12(11), 2870–2878.

84. Han, P., Dong, D., Zhao, X., Jiao, L., & Lang, Y. (2016). A smartphone-based soil color sensor: For soil type classification. Computers and Electronics in Agriculture, 123, 232–241.

85. Burton, L., Jayachandran, K., & Bhansali, S. (2020). Review—The “Real-Time” Revolution for In situ Soil Nutrient Sensing. Journal of The Electrochemical Society, 167(3), 037569.

86. Meivel, S., & Maheswari, S. (2021). Remote Sensing Analysis of Agricultural Drone. Journal of the Indian Society of Remote Sensing, 49(3), 689–701.

87. Tyagi, A., Reddy, A. A., Singh, J., & Chowdhury, S. R. (2011). A low cost portable temperature-moisture sensing unit with artificial neural network based signal conditioning for smart irrigation applications. International Journal on Smart Sensing and Intelligent Systems, 4(1), 94–111.

88. Goldstein, A., Fink, L., Meitin, A., Bohadana, S., Lutenberg, O., & Ravid, G. (2018). Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precision Agriculture, 19(3), 421–444.

89. Zhang, P., Zhang, Q., Liu, F., Li, J., Cao, N., & Song, C. (2017). The Construction of the Integration of Water and Fertilizer Smart Water Saving Irrigation System Based on Big Data. Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017, 2, 392–397.

90. Hashemi, M., & Sepaskhah, A. R. (2020). Evaluation of artificial neural network and Penman–Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region. Theoretical and Applied Climatology, 139(1), 275–285.

91. Chen, X., Qi, Z., Gui, D., Sima, M. W., Zeng, F., Li, L., … Gu, Z. (2020). Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate. Agricultural Water Management, 234(October 2019), 106139.

92. Yang, G., Liu, L., Guo, P., & Li, M. (2017). A flexible decision support system for irrigation scheduling in an irrigation district in China. Agricultural Water Management, 179, 378–389.

93. Carrión, F., Tarjuelo, J. M., Carrión, P., & Moreno, M. A. (2013). Low-cost microirrigation system supplied by groundwater: An application to pepper and vineyard crops in Spain. Agricultural Water Management, 127, 107–118.

94. G. Evans, R., M. Iversen, W., & Kim, Y. (2011). Integrated Decision Support, Sensor Networks, and Adaptive Control for Wireless Site-Specific Sprinkler Irrigation. Applied Engineering in Agriculture, 28(3), 377–387.

95. Shu, J., Liao, H. H., & Xu, Y. F. (2015). Water-Saving Monitoring System Design Based on LabView Simulation Platform. Applied Mechanics and Materials, 742, 582–585.

96. Ramos-Fernández, J. C., Balmat, J. F., Márquez-Vera, M. A., Lafont, F., Pessel, N., & Espinoza-Quesada, E. S. (2016). Fuzzy Modeling Vapor Pressure Deficit to Monitoring Microclimate in Greenhouses. IFAC-PapersOnLine, 49(16), 371–374.

97. Seenu, N., Chetty, R. M. K., Srinivas, T., Krishna, K. M. A., & Selokar, A. (2019). Reference Evapotranspiration Assessment Techniques for Estimating Crop Water Requirement. International Journal of Recent Technology and Engineering, 8(4), 1094–1100.

98. Villarrubia, G., De Paz, J. F., De La Iglesia, D. H., & Bajo, J. (2017). Combining multi-agent systems and wireless sensor networks for monitoring crop irrigation. Sensors (Switzerland), 17(8).

99. Ding, S., Li, H., Su, C., Yu, J., & Jin, F. (2013). Evolutionary artificial neural networks: a review Artificial Intelligence Review, 39(3), 251–260.

100. Dursun, M., & Özden, S. (2014). An efficient improved photovoltaic irrigation system with artificial neural network based modeling of soil moisture distribution - A case study in Turkey. Computers and Electronics in Agriculture, 102, 120–126.

101. Baba, A. P. A., Shiri, J., Kisi, O., Fard, A. F., Kim, S., & Amini, R. (2013). Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1), 131–146.

102. Poyen, F. Bin, Roy, S., Ghosh, A., & Bandyopadhyay, R. (2015). Automated irrigation by an ANN controller. Procedia Computer Science, 46(Icict 2014), 257–267.

103. Dela Cruz, J. R., Magsumbol, J. A. V., Dadios, E. P., Baldovino, R. G., Culibrina, F. B., & Lim, L. A. G. (2017). Design of a fuzzy-based automated organic irrigation system for smart farm. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, 2018-Janua, 1–6.

104. Wang, Y., Lu, Y., & Xiao, R. (2021). Application of nonlinear adaptive control in temperature of chinese solar greenhouses. Electronics (Switzerland), 10(13).