{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:56:11Z","timestamp":1766138171536},"reference-count":24,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>The efficient management of water resources is a major issue in the field of sustainable development. Several models of solving this problem can be found in the literature, especially in the agricultural sector which represents the main consumer through irrigation. Therefore, Irrigation management is an important and innovative field that has been the subject of several types of research and studies to deal with the different activities, behaviors, and conflicts between the different users. This article introduces an intelligent irrigation system based on smart sensors that can be used moderately and economically to monitor farms by integrating some connected electronic devices and other advantageous instruments widely used in the field of IoT, it determines the water requirement of each farm according to the water loss due to the process of evapotranspiration. The water requirement is calculated from data collected from a series of sensors installed in the plantation farm. This project focuses on smart irrigation based on IoT and agent technology, it can be used by farmer associations whose endowments and irrigation planning are defined according to the need and quantity of water available in the rural municipality. The system includes a microcontroller with the integration of sensors, actuators, and valve modules where each node serves as an IoT device. Environmental parameters are monitored directly through a multi-agent system that facilitates the control of each node and the configuration of irrigation parameters. The amount of water calculated for irrigation is based on the Penman model for calculating the daily evapotranspiration baseline. Compared to the conventional irrigation method, it is expected that the proposed irrigation model would contribute to saving water use and distributing it impartially without compromising its production.<\/jats:p>","DOI":"10.2298\/csis220227062i","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T14:49:21Z","timestamp":1671806961000},"page":"405-421","source":"Crossref","is-referenced-by-count":5,"title":["Internet of things and agent-based system to improve water use efficiency in collective irrigation"],"prefix":"10.2298","volume":"20","author":[{"given":"Abdelouafi","family":"Ikidid","sequence":"first","affiliation":[{"name":"Computer Science Dept, Computing Systems Engineering Laboratory (LISI), Cadi Ayyad University Marrakesh, Morocco"}]},{"suffix":"Abdelaziz","given":"Fazziki","family":"El","sequence":"additional","affiliation":[{"name":"Computer Science Dept, Computing Systems Engineering Laboratory (LISI), Cadi Ayyad University Marrakesh, Morocco"}]},{"given":"Mohamed","family":"Sadgal","sequence":"additional","affiliation":[{"name":"Computer Science Dept, Computing Systems Engineering Laboratory (LISI), Cadi Ayyad University Marrakesh, Morocco"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"B. 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June, pp. 69-73, 2020, doi: 10.1109\/I2CACIS49202.2020.9140199.","DOI":"10.1109\/I2CACIS49202.2020.9140199"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"M. E. P\u00e9rez-pons, R. S. Alonso, O. Garc\u00eda, G. Marreiros, and J. M. Corchado, \u201cDeep QLearning and Preference Based Multi-Agent System for Sustainable Agricultural Market,\u201d pp. 1-16, 2021.","DOI":"10.3390\/s21165276"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"F. Almomani, \u201cPrediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network,\u201d Fuel, vol. 280, no. April, p. 118573, 2020, doi: 10.1016\/j.fuel.2020.118573.","DOI":"10.1016\/j.fuel.2020.118573"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Y. W. Ma, J. Q. Shi, J. L. Chen, C. C. Hsu, and C. H. 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Hongal, and M. P. Jyothi, \u201cIOT Based Monitoring System in Smart Agriculture,\u201d in Proceedings - 2017 International Conference on Recent Advances in Electronics and Communication Technology, ICRAECT 2017, 2017, doi: 10.1109\/ICRAECT.2017.52.","DOI":"10.1109\/ICRAECT.2017.52"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"P. C. Sentelhas, T. J. Gillespie, and E. A. Santos, \u201cEvaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada,\u201d Agricultural Water Management, vol. 97, no. 5, 2010, doi: 10.1016\/j.agwat.2009.12.001.","DOI":"10.1016\/j.agwat.2009.12.001"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"A. Ikidid, A. El Fazziki, and M. 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Bauder, \u201cIrrigation Scheduling: The Water Balance Approach,\u201d Colorado State University Extension, no. 4, pp. 1-6, 2011."}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T06:33:25Z","timestamp":1721889205000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142200062I"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.2298\/csis220227062i","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}