{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:47:28Z","timestamp":1775666848232,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.<\/jats:p>","DOI":"10.3390\/s21093079","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T22:29:07Z","timestamp":1619648947000},"page":"3079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-4392","authenticated-orcid":false,"given":"Andr\u00e9","family":"Gl\u00f3ria","sequence":"first","affiliation":[{"name":"Department of Science, Information and Technology, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal"},{"name":"Instituto de Telecomuni\u00e7\u00f5es (IT), 1049-001 Lisbon, Portugal"}]},{"given":"Jo\u00e3o","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Department of Science, Information and Technology, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7729-4033","authenticated-orcid":false,"given":"Pedro","family":"Sebasti\u00e3o","sequence":"additional","affiliation":[{"name":"Department of Science, Information and Technology, Instituto Universit\u00e1rio de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal"},{"name":"Instituto de Telecomuni\u00e7\u00f5es (IT), 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (2021, March 21). Water for Sustainable Food and Agriculture. Available online: http:\/\/www.fao.org\/3\/a-i7959e.pdf."},{"key":"ref_2","unstructured":"United States Environmental Protection Agency (2021, March 21). Why Save Water? Statistics and Facts, Available online: https:\/\/www.epa.gov\/watersense\/statistics-and-facts."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications","volume":"17","author":"Guizani","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shkurti, L., Bajrami, X., Canhasi, E., Limani, B., Krrabaj, S., and Hulaj, A. (2017, January 11\u201315). Development of ambient environmental monitoring system through wireless sensor network (WSN) using NodeMCU and \u2019WSN monitoring\u2019. Proceedings of the 2017 6th Mediterranean Conference on Embedded Computing, MECO 2017\u2014Including ECYPS 2017, Bar, Montenegro.","DOI":"10.1109\/MECO.2017.7977235"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cardoso, J., Gl\u00f3ria, A., and Sebasti\u00e3o, P. (2020, January 25\u201327). A Methodology for Sustainable Farming Irrigation using WSN, NB-IoT and Machine Learning. Proceedings of the 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Corfu, Greece.","DOI":"10.1109\/SEEDA-CECNSM49515.2020.9221791"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., and Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res., 119.","DOI":"10.1016\/j.cor.2020.104926"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.procs.2020.03.440","article-title":"IoT and Machine Learning Approaches for Automation of Farm Irrigation System","volume":"167","author":"Vij","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gl\u00f3ria, A., Sebasti\u00e3o, P., Dion\u00edsio, C., Sim\u00f5es, G., and Cardoso, J. (2020). Water management for sustainable irrigation systems using internet-of-things. Sensors, 20.","DOI":"10.3390\/s20051402"},{"key":"ref_9","unstructured":"Espressif Systems (2021, March 21). ESP32 Series Datasheet. Available online: https:\/\/www.espressif.com\/sites\/default\/files\/documentation\/esp32_datasheet_en.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ali, A.I., Partal, S.Z., Kepke, S., and Partal, H.P. (2019, January 12\u201315). ZigBee and LoRa based Wireless Sensors for Smart Environment and IoT Applications. Proceedings of the 2019 IEEE 1st Global Power, Energy and Communication Conference (GPECOM2019), Nevsehir, Turkey.","DOI":"10.1109\/GPECOM.2019.8778505"},{"key":"ref_11","unstructured":"HopeRF (2021, March 21). RFM95W Datasheet. Available online: https:\/\/cdn.sparkfun.com\/assets\/learn_tutorials\/8\/0\/4\/RFM95_96_97_98W.pdf."},{"key":"ref_12","unstructured":"Silicon Labs (2021, March 21). Si7021-A20 Datasheet. Available online: https:\/\/www.silabs.com\/documents\/public\/data-sheets\/Si7021-A20.pdf."},{"key":"ref_13","unstructured":"Electronics, D. (2021, March 21). DS18B20 Datasheet. Available online: https:\/\/pdf1.alldatasheet.com\/datasheet-pdf\/view\/227472\/DALLAS\/DS18B20.html."},{"key":"ref_14","unstructured":"DFRobot (2021, March 21). Capacity Soil Moisture Sensor Datasheet. Available online: https:\/\/wiki.dfrobot.com\/Capacitive_Soil_Moisture_Sensor_SKU_SEN0193."},{"key":"ref_15","unstructured":"Solargis (2020, July 20). Solargis\u2014High Resolution Solar Data. Available online: https:\/\/solargis.info."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Patil, T.G., and Asokan, S. (2017, January 23\u201324). Comparative analysis of calculation of solar panel efficiency degradation. Proceedings of the ICONSTEM 2017\u2014Proceedings: 3rd IEEE International Conference on Science Technology, Engineering and Management, Chennai, India.","DOI":"10.1109\/ICONSTEM.2017.8261377"},{"key":"ref_17","unstructured":"DFRobot (2021, February 21). Weather Station Datasheet. Available online: https:\/\/wiki.dfrobot.com\/Weather_Station_with_Anemometer_Wind_vane_Rain_bucket_SKU_SEN0186."},{"key":"ref_18","unstructured":"Food and Agriculture Organization of the United Nations (2020, October 18). Single Crop Coefficient (Kc). Available online: http:\/\/www.fao.org\/3\/x0490e\/x0490e0b.htm."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hargreaves, G., and Samani, Z. (1985). Reference Crop Evapotranspiration From Temperature. Appl. Eng. Agric., 1.","DOI":"10.13031\/2013.26773"},{"key":"ref_20","unstructured":"Food and Agriculture Organization of the United Nations (2020, October 18). Meteorological Tables. Available online: http:\/\/www.fao.org\/3\/X0490E\/x0490e0j.htm."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.09.013","article-title":"Machine learning algorithms for wireless sensor networks: A survey","volume":"49","author":"Amgoth","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s11119-017-9527-4","article-title":"Applying machine learning on sensor data for irrigation recommendations: Revealing the agronomist\u2019s tacit knowledge","volume":"19","author":"Goldstein","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mamdouh, M., Elrukhsi, M.A., and Khattab, A. (2018, January 25\u201325). Securing the Internet of Things and Wireless Sensor Networks via Machine Learning: A Survey. Proceedings of the 2018 International Conference on Computer and Applications, ICCA 2018, Beirut, Lebanon.","DOI":"10.1109\/COMAPP.2018.8460440"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, F.J. (2019, January 5\u20137). An extended idea about decision trees. Proceedings of the 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019, Las Vegas, NV, USA.","DOI":"10.1109\/CSCI49370.2019.00068"},{"key":"ref_25","unstructured":"Tang, Q., Ge, X., and Liu, Y.C. (2016, January 22\u201326). Performance analysis of two different SVM-based field-oriented control schemes for eight-switch three-phase inverter-fed induction motor drives. Proceedings of the 2016 IEEE 8th International Power Electronics and Motion Control Conference, IPEMC-ECCE Asia 2016, Hefei, China."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Saravanan, R., and Sujatha, P. (2018, January 14\u201315). A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8663155"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2008). Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_28","unstructured":"Scikit-Learn (2021, February 16). Scikit-Learn. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_29","unstructured":"Scikit-Learn (2021, February 16). RandomizedSearchCV. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RandomizedSearchCV.html."},{"key":"ref_30","unstructured":"IPMA (2020, July 31). IPMA\u2014API. Available online: http:\/\/api.ipma.pt\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cardoso, J., Gloria, A., and Sebastiao, P. (2020, January 26\u201327). Improve Irrigation Timing Decision for Agriculture using Real Time Data and Machine Learning. Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, Sakheer, Bahrain.","DOI":"10.1109\/ICDABI51230.2020.9325680"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Adeyemi, O., Grove, I., Peets, S., Domun, Y., and Norton, T. (2018). Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling. Sensors, 18.","DOI":"10.3390\/s18103408"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Campos, N.G., Rocha, A.R., Gondim, R., da Silva, T.L., and Gomes, D.G. (2020). Smart & green: An internet-of-things framework for smart irrigation. Sensors, 20.","DOI":"10.3390\/s20010190"},{"key":"ref_34","unstructured":"Food and Agriculture Organization of the United Nations (2020, September 20). Optimizing Soil Moisture for Plant Production. Available online: http:\/\/www.fao.org\/3\/a-y4690e.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3456","DOI":"10.1002\/agj2.20246","article-title":"Irrigation scheduling technologies reduce water use and maintain turfgrass quality","volume":"112","author":"Serena","year":"2020","journal-title":"Agron. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Froiz-M\u00edguez, I., Lopez-Iturri, P., Fraga-Lamas, P., Celaya-Echarri, M., Blanco-Novoa, O., Azpilicueta, L., Falcone, F., and Fern\u00e1ndez-Caram\u00e9s, T.M. (2020). Design, implementation, and empirical validation of an IoT smart irrigation system for fog computing applications based on Lora and Lorawan sensor nodes. 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