Development of a Fuzzy Case-Based Reasoning Decision Support System for Water Management in Smart Agriculture
Keywords:
Decision support system, smart agriculture, smart irrigation, case-based reasoning (CBR)Abstract
This paper proposes a decision support system aimed at improving water management in smart agriculture, utilizing the Case-Based Reasoning (CBR) methodology. Given the increasing challenges of water resources and the need for their optimal use in agriculture, the application of advanced technologies for smart resource management has gained significant importance. The proposed system assists in better decision-making regarding irrigation timing and quantity by collecting data from various sensors, including information about environmental conditions, soil status, and plant water needs. As part of the system, the case-based reasoning model uses historical data and similarity comparison between current situations and previous cases to offer optimal water management solutions. The Internet of Things (IoT), as the main infrastructure of this system, facilitates the continuous and real-time collection of data, thereby enhancing the accuracy of decisions. The results obtained show that this system can optimize water consumption, reduce irrigation costs, and increase agricultural productivity. The key findings of this study suggest that this approach could serve as a sustainable solution for water efficiency in smart agriculture and optimal water resource management in the future.
References
X. Cai, X. Zhang, P. H. Noël, and M. Shafiee‐Jood, "Impacts of climate change on agricultural water management: a review," Wiley Interdisciplinary Reviews: Water, vol. 2, no. 5, pp. 439-455, 2015, doi: 10.1002/wat2.1089.
Y. Ahansal, M. Bouziani, R. Yaagoubi, I. Sebari, K. Sebari, and L. Kenny, "Towards smart irrigation: A literature review on the use of geospatial technologies and machine learning in the management of water resources in arboriculture," Agronomy, vol. 12, no. 2, p. 297, 2022, doi: 10.3390/agronomy12020297.
I. Adhicandra, T. Tanwir, A. Asfahani, J. W. Sitopu, and F. Irawan, "Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries," Innovative: Journal Of Social Science Research, vol. 4, no. 3, pp. 1027-1037, 2024, doi: 10.31004/innovative.v4i3.10551.
M. Singh and S. Ahmed, "IoT based smart water management systems: A systematic review," Materials Today: Proceedings, vol. 46, pp. 5211-5218, 2021, doi: 10.1016/j.matpr.2020.08.588.
S. Asgharinezhad, H. Rezghi Shirsavar, and K. Khanzadi, "Identifying the Dimensions and Components of Internet of Things (IoT) Development in Schools Based on Futurology," (in eng), Iranian Journal of Educational Sociology, Research Article vol. 7, no. 2, pp. 98-105, 2024, doi: 10.61838/kman.ijes.7.2.12.
S. Asgharinezhad, H. Rezghi Shirsavar, and K. Khanzadi, "Investigating the Status of Internet of Things Development in Schools based on the Future Research," Sociology of Education, vol. 10, no. 1, pp. 152-160, 2024, doi: 10.22034/ijes.2024.2017649.1517.
V. K. Quy et al., "IoT-enabled smart agriculture: architecture, applications, and challenges," Applied Sciences, vol. 12, no. 7, p. 3396, 2022, doi: 10.3390/app12073396.
A. Aamodt and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches," AI communications, vol. 7, no. 1, pp. 39-59, 1994, doi: 10.3233/AIC-1994-7104.
J. Gómez and et al., "Enhancing irrigation efficiency through case-based reasoning systems," Computers and Electronics in Agriculture, vol. 193, p. 106853, 2023.
R. Hemalatha, G. Deepika, D. Dhanalakshmi, K. Dharanipriya, and M. Divya, "Internet of things (iot) based smart irrigation," International Journal Of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), vol. 2, no. 2, pp. 128-132, 2016.
S. Begum, S. Barua, R. Filla, and M. U. Ahmed, "Classification of physiological signals for wheel loader operators using multi-scale entropy analysis and case-based reasoning," Expert Syst. Appl., vol. 41, pp. 295-306, 2014, doi: 10.1016/j.eswa.2013.05.068.
R. Janssen, P. Spronck, and A. Arntz, "Case-based reasoning for predicting the success of therapy," Expert Syst., vol. 32, pp. 165-177, 2015, doi: 10.1111/exsy.12074.
M. A. Mohammed et al., "Genetic case-based reasoning for improved mobile phone faults diagnosis," Comput. Electr. Eng., vol. 71, pp. 212-222, 2018, doi: 10.1016/j.compeleceng.2018.07.053.
Z. W. Zhong, T. H. Xu, F. Wang, and T. Tang, "Text case-based reasoning framework for fault diagnosis and prediction by cloud computing," Math. Probl. Eng., 2018, doi: 10.1155/2018/9464971.
K. Amailef and J. Lu, "Ontology-based case-based reasoning approach for intelligent m-Government emergency response services," Decis. Support. Syst., vol. 55, pp. 79-97, 2013, doi: 10.1016/j.dss.2012.12.034.
F. Yu, X. Y. Li, and X. S. Han, "Risk response for urban water supply network using case-based reasoning during a natural disaster," Saf. Sci., vol. 106, pp. 121-139, 2018, doi: 10.1016/j.ssci.2018.03.003.
F. Le Ber, A. Napoli, J. L. Metzger, and S. Lardon, "Modeling and comparing farm maps using graphs and case-based reasoning," J. Univers. Comput. Sci., vol. 9, pp. 1073-1095, 2003.
J. Evans, A. Terhorst, and B. H. Kang, "From data to decisions: Helping crop producers build their actionable knowledge," Crit. Rev. Plant. Sci., vol. 36, pp. 71-88, 2017, doi: 10.1080/07352689.2017.1336047.
D. A. Sharaf-Eldeen, I. F. Moawad, K. El Bahnasy, and M. E. Khalifa, "Learning and applying range adaptation rules in case-based reasoning systems," 2012, doi: 10.1007/978-3-642-35326-0_48.
X. Li and A. G. Yeh, "Multitemporal SAR images for monitoring cultivation systems using case-based reasoning," Remote Sens. Environ., vol. 90, no. 4, pp. 524-534, 2004, doi: 10.1016/j.rse.2004.01.018.
E. K. Gebre-Amanuel, F. G. Taddesse, and A. T. Assalif, "Web based expert system for diagnosis of cattle disease," 2018, doi: 10.1145/3281375.3281400.
Q. Han, "Development and application of remote intelligent diagnosis mobile phone system for animal diseases," Rev. Cient. CIENCIAS Vet., vol. 29, no. 2, pp. 393-401, 2019.
C. Zhu and G. Yin, "A prediction method of crop diseases and insect pests based on ontological case reasoning," Rev. Fac. Agron., vol. 36, no. 6, pp. 1720-1731, 2019.
A. Gonzalez-Briones, J. A. Castellanos-Garzon, Y. Mezquita-Martin, J. Prieto, and J. M. Corchado, "A multi-agent system framework for autonomous crop irrigation," 2019, doi: 10.1109/CAIS.2019.8769456.
N. J. Car and G. A. Moore, "Bridging the gap between modelling advice and irrigator solutions through empirical reasoning techniques," 2011.
I. Watson and F. Marir, "Case-based reasoning-A review," Knowl. Eng. Rev., vol. 9, pp. 327-354, 1994, doi: 10.1017/S0269888900007098.
S. Dutta, B. Wierenga, and A. Dalebout, "Case-based reasoning: From automation to decision-aiding and simulation," IEEE Trans. Knowl. Data Eng., vol. 9, pp. 911-922, 1997, doi: 10.1109/69.649316.
