Review of Knowledge-Based Management System for Irrigation Scheduling Modeled Upon Reduced Parametric Estimates

Abstract

Irrigation scheduling is the process of ensuring appropriate, adequate and proportionate crops. Water Management (CWM) stands very important for its water management capability and crop yield optimization among several other advantages. Efficient water management is always crucial for sustainable agricultural practices, traditional irrigation methods often lead to water wastage and suboptimal crop yields Hence, the adoption of technological advancement that spans from the traditional and manual mode to automation, to the application of IOT and extends to the use of Artificial Intelligence (AI). The review paper considers using knowledge-based algorithms for irrigation scheduling, focusing on those that need fewer input parameters. The review looks at several different kinds of knowledge-based algorithms, such as Fuzzy Logic Control, Expert Systems, Neural Networks, Genetic Algorithms, Decision Trees, and Reinforcement learning. The review highlights the fact that knowledge-based algorithms could be a great alternative to traditional irrigation scheduling models, especially when it comes to places where there are few resources for computing power or getting the right data. It also talks about the challenges that come with using these algorithms. Overall, the review makes a strong case for using knowledge-based algorithms for irrigation scheduling. It discusses the tools and techniques used to make these algorithms work well and offers some advice on how to ensure they're being used in the best possible way.

Keywords

• Knowledge-based irrigation algorithms
• Sustainable water management
• Artificial intelligence in agriculture

References

1. Abioye EA, Hensel O, Esau TJ, Elijah O, Abidin MSZ, Ayobami AS, Yerima O and Nasirahmadi A (2022). Precision irrigation management using machine learning and digital farming solutions. AgriEngineering, 4(1): 70-103.
2. Adel ZT, Carmi G and Ünlü M (2020). Promising water management strategies for arid and semiarid environments. DOI: 10.5772/intechopen.87103.selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML)
3. Agyeman BT, Nouri M, Appels WM, Liu J and Shah SL (2024), Learning-based multi-agent MPC for irrigation scheduling. Control Engineering Practice, 147: 105908. https://doi.org/10.1016/j.conengprac.2024.105908
4. Ali A, Hussain T, Tantashutikun N, Hussain N and Cocetta G (2023). Application of smart techniques, internet of things and data mining for resource use efficient and sustainable crop production. Agriculture, 13(2), 397. https://doi.org/10.3390/agriculture13020397
5. Ayodele AT, Bolaji BO, Arowolo MO and Olanipekun MU (2021), Overview of sensor analysis for health monitoring -an expert system for catfish pond. IOP Conference Series Materials Science and Engineering 1107(1). Conference: ICESW2020, May 2021. https://doi.org/10.1088/1757-899X/1107/1/012065
6. Benzaouia M, Hajji B, Mellit A and Rabhi A (2023), Fuzzy-IoT smart irrigation system for precision scheduling and monitoring, Computers and Electronics in Agriculture, 215: 108407.
7. Blessy JA (2021). Smart irrigation system techniques using artificial intelligence and iota. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 1355-1359). IEEE.
8. Borsato E, Rosa L, Marinello F Taroll P and D'Odorico P (2020). Weak and strong sustainability of irrigation: A framework for irrigation practices under limited water availability. Frontiers in Sustainable Food Systems, 4: 17.

9. Brouwer R, Hofkes M and Linderho V (2018). General equilibrium modeling of the direct and indirect economic impacts of water quality improvements in the Netherlands. Economic Modeling, 69: 296-309.
10. Buchanan BG and Shortliffe EH (1984). Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley Longman Publishing Co., Inc.
11. Darouich H, Cameira MR, Charged CM and Pereira LS (2017). Drip vs. surface irrigation: A comparison focusing on water saving and economic returns using multicriteria analysis applied to cotton. Biosystems Engineering, 159: 78-95.
12. Deb D (2020). Is the system of rice intensification (SRI) consonant with agroecology? Agroecology and Sustainable Food Systems, 44(10): 1338–1369. https://doi.org/10.1080/21683565.2020.177916
13. Deb K Pratap A, Agarwal S and Meyarivan T (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182-197.
14. Durkin, J. (1994). Expert systems: design and development. Prentice-Hall, Inc.
15. Guo Y, Zhang, Z, Gao J, Qi Z and Jiang T (2021). Development of a rule-based irrigation scheduling system for winter wheat in the North China Plain. Agricultural Water Management, 243: 106449.
16. Hanyu Wei, Wen Xu, Byeong Kang, Rowan Eisner, Albert Muleke, Daniel Rodriguez, Peter deVoil, Victor Sadras, Marta Monjardino & Matthew Tom Harrison (2024) Irrigation with Artificial Intelligence: Problems, Premises, Promises, Human-Centric Intelligent Systems, Volume 4, pages 187–205.
17. Hedley CB, Yule IJ, Tuohy M and Vogeler I (2009). Key performance indicators for variable rate irrigation implementation on variable soils. American Society of Agricultural and Biological Engineers Annual International Meeting 2009, ASABE 2009. 6. 10.13031/2013.27439.
18. Jha A, Kaur S, Kumari S and Sharma M (2023). IoT-based irrigation management system. Book Chapter. https://doi.org/10.58532/v2bs9ch15
19. Giarratano JC and Riley GD (2005). Expert systems: Principles and programming. Brooks/Cole Publishing Co., USA.
20. Karimi A, Lashgari H, Liaghat A and Zand-Parsa S (2021). Knowledge-based algorithms for irrigation scheduling: A review. Agricultural Water Management, 244: 106532.
21. Khriji, S., El Houssaini, D., Kammoun, I., Kanoun, O. (2021). Precision Irrigation: An IoT-Enabled Wireless Sensor Network for Smart Irrigation Systems. In: Hamrita, T. (eds) Women in Precision Agriculture. Women in Engineering and Science. Springer, Cham. https://doi.org/10.1007/978-3-030-49244-1_6
22. Kunapara AN, Subbaiah R, Prajapati GV and Makwana JJ (2016). Influence of Drip Irrigation Regimes and Lateral Spacing on Cumin Productivity. Current World Environment, 11(1): 333-337. http://doi.org/10.12944/CWE.11.1.40
23. Li H, Yang X, Chen H, Cui Q, Yuan G, Han X, Wei C, Zhang Y, Ma J and Zhang X (2018). Water requirement characteristics and the optimal irrigation schedule for the growth, yield, and fruit quality of watermelon under plastic film mulching. Scientia Horticulturae, 241: 74-82. https://doi.org/10.1016/j.scienta.2018.06.067)
24. Li Y Du H and Kumaraswamy SB (2023) Case-based reasoning approach for decision-making in building retrofit: A review. Building and Environment, 248: 111030.
25. Lozano D, Mateos L, Orgaz F and Fernández E (2020). Modeling and improving irrigation scheduling for wheat. Water, 12(1): 163.
26. Mallareddy M, Thirumalaikumar R, Balasubramanian P, Naseeruddin R, Nithya N, Mariadoss A and Vijayakumar S (2023). Maximizing water use efficiency in rice farming: A comprehensive review of innovative irrigation management technologies. Water, 15(10): 1802.
27. Obaideen K, Yousef BA, Al Mallahi MN, Tan YC, Mahmoud M, Jaber H and Ramadan M (2022). An overview of smart irrigation systems using IoT. Energy Nexus, 7: 100124. https://doi.org/10.1016/j.nexus.2022.100124
28. Ogidan OK, Onile AE and Adegboro OG (2019). Smart irrigation system: A water management procedure. Agricultural Sciences, 10: 25-31. https://doi.org/10.4236/as.2019.101003
29. Ogidan OK, Oluwagbayide SD and Ale TO (2023). A Microcontroller-Based Irrigation Scheduling Using FAO Penman-Monteith Equation. Turkish Journal of Agricultural Engineering Research (TURKAGER), 4(1): 15-25. https://doi.org/10.46592/turkager.1170630
30. Parmar SH, Tiwari MK, Pargi DL, Pampaniya NK and Rajani NV (2019). Modeling the land surface temperature using thermal remote sensing at Godhra, Gujarat. Journal of Agrometeorology, 21(1): 107-109. https://doi.org/10.54386/jam.v21i1.216
31. Rouhani OR, Javadikia H, Tabari M, Mosavi A and Varkonyi-Koczy AR (2012). Modeling of sprinkler irrigation runoff using ANFIS. Arabian Journal for Science and Engineering, 37(8): 2265-2273.
32. Sahoo S Agyeman BT, Debnath S and Liu J (2022). Knowledge-based optimal irrigation scheduling of three-dimensional agro-hydrological systems. IFAC-PapersOnLine, 55(7): 441-446. https://doi.org/10.1016/j.ifacol.2022.07.483
33. Sharifnasab, H.; Mahrokh, A.; Dehghanisanij, H.; Łazuka, E.; Łagód, G.; Karami, H. (2023) Evaluating the Use of Intelligent Irrigation Systems Based on the IoT in Grain Corn Irrigation. Water 15, 1394. https://doi.org/10.3390/w15071394
34. Shiri J, Keshavarzi A, Kisi O Jahanzad AE and Bagherzadeh A (2017). Modeling soil water content using neuro-fuzzy, neural network, and regression techniques. Soil and Tillage Research, 165, 11-20.
35. Shrestha NK, Shukla S, Daggupati P Mekonnen B and Rudra RP (2020). Watershed modeling for sustainable agriculture: A review. Water, 12(8): 2163.
36. Tace Y, Elfilali S, Tabaa M and Leghris C (2023), Implementation of smart irrigation using IoT and Artificial Intelligence, Mathematical Modeling and Computing, 10(2): 575–582
37. Taghvaeian S, Comas L DeJonge KC and Trout TJ (2014). Conventional and simplified canopy temperature indices predict water stress in sunflower. Agricultural Water Management, 144: 69-80.
38. Talaviya T, Shah D, Patel N, Yagnik Hand Shah M (2020). Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4: 58-73. https://doi.org/10.1016/j.aiia.2020.04.002).
39. Vallejo-Gómez D, Osorio M and Hincapié CA (2023). Smart irrigation systems in agriculture: A Systematic Review. Agronomy, 13(2): 342.
40. Velmurugan A, Swarnam P, Subramani T, Babulal Meena B and Kaledhonkar M (2024). Water demand and salinity. Desalination-Challenges and Opportunities. IntechOpen, Edited by Mohammad Hossein Davood Abadi Farahani, Vahid Vatanpour and Amir Hooshang Taheri. ISBN: 978-1-78984-739-0, p. 130. https://doi.org/10.5772/intechopen.88095
41. Zhaoyu Z, José F, Martínez N, Lucas M and Vicente HD (2020). Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming. Computers and Electronics in Agriculture, 178: 105741.