{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:58:32Z","timestamp":1774969112340,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"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>Enhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies.<\/jats:p>","DOI":"10.3390\/s25227079","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:02:42Z","timestamp":1763640162000},"page":"7079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Edge-Computing Smart Irrigation Controller Using LoRaWAN and LSTM for Predictive Controlled Deficit Irrigation"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5567-9194","authenticated-orcid":false,"given":"Carlos Cambra","family":"Baseca","sequence":"first","affiliation":[{"name":"Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalizacion, Escuela Politecnica Superior, Universidad de Burgos, Av. Cantabria s\/n, 09006 Burgos, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6810-2447","authenticated-orcid":false,"given":"Rog\u00e9rio","family":"Dion\u00edsio","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, School of Technology, Av. do Empres\u00e1rio s\/n, 6000-767 Castelo Branco, Portugal"},{"name":"Centro de Investiga\u00e7\u00e3o em Servi\u00e7os Digitais (CISeD), Av. Cor. Jos\u00e9 Maria Vale de Andrade, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1225-3844","authenticated-orcid":false,"given":"Fernando","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, School of Technology, Av. do Empres\u00e1rio s\/n, 6000-767 Castelo Branco, Portugal"},{"name":"Centro de Investiga\u00e7\u00e3o em Servi\u00e7os Digitais (CISeD), Av. Cor. Jos\u00e9 Maria Vale de Andrade, 3504-510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7327-2109","authenticated-orcid":false,"given":"Jos\u00e9","family":"Metr\u00f4lho","sequence":"additional","affiliation":[{"name":"Polytechnic Institute of Castelo Branco, School of Technology, Av. do Empres\u00e1rio s\/n, 6000-767 Castelo Branco, Portugal"},{"name":"Centro de Investiga\u00e7\u00e3o em Servi\u00e7os Digitais (CISeD), Av. Cor. Jos\u00e9 Maria Vale de Andrade, 3504-510 Viseu, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization of the United Nations (FAO) (2017). The State of Food and Agriculture 2017: Leveraging Food Systems for Inclusive Rural Transformation, FAO. 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