{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T13:32:09Z","timestamp":1785850329703,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T00:00:00Z","timestamp":1707523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"national food security research program (PNR)"},{"name":"PRIMA program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The agricultural sector faces several difficulties today in ensuring the safety of food supply, including water scarcity. This study presents the design and development of a low-cost and full-featured fog-IoT\/AI system targeted towards smallholder farmer communities (SFCs). However, the smallholder community is hesitant to adopt technology-based solutions. There are many overwhelming reasons for this, but the high cost, implementation complexity, and malfunctioning sensors cause inappropriate decisions. The PRIMA INTEL-IRRIS project aims to make digital and innovative agricultural technologies more appealing and available to these communities by advancing the intelligent irrigation \u201cin-the-box\u201d concept. Considered a vital resource, collected data are used to detect anomalies or abnormal behavior, providing information about an occurrence or a node failure. To prevent agro-field data leakage, this paper presents an innovative, smart, and sustainable low-cost irrigation system that employs artificial intelligence (AI) techniques to analyze anomalies and problems in water usage. The sensor anomaly can be detected using an autoencoder (AE) and a generative adversarial network (GAN). We will feed the autoencoders\u2019 anomaly detection models with time series records from the datasets and replace detected anomalies with the reconstructed outputs. When integrated with an IoT platform, this methodology is a tool for easing the labeling of sensor anomalies and can help create supervised datasets for future research. In addition, anomalies can be corrected by prediction models based on deep learning approaches, applying CNN\/BiLSTM architecture. The results show that AEs outperform the GANs, achieving an accuracy of 90%, 95%, and 97% for soil moisture, air temperature, and air humidity, respectively. The proposed system is designed to ensure that the data are of high quality and reliable enough to make sound decisions compared to the existing platforms.<\/jats:p>","DOI":"10.3390\/s24041162","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:50:27Z","timestamp":1707709827000},"page":"1162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["An Innovative Smart and Sustainable Low-Cost Irrigation System for Anomaly Detection Using Deep Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7617-9057","authenticated-orcid":false,"given":"Rabaie","family":"Benameur","sequence":"first","affiliation":[{"name":"Research Laboratory in Industrial Computing and Networks (RIIR), University of Oran 1, B.P. 1524, El M\u2019Naouer, Oran 31000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6998-208X","authenticated-orcid":false,"given":"Amine","family":"Dahane","sequence":"additional","affiliation":[{"name":"Research Laboratory in Industrial Computing and Networks (RIIR), University of Oran 1, B.P. 1524, El M\u2019Naouer, Oran 31000, Algeria"},{"name":"Institute of Applied Science and Technology, ISTA, University of Oran 1, B.P. 1524, El M\u2019Naouer, Oran 31000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8635-4667","authenticated-orcid":false,"given":"Bouabdellah","family":"Kechar","sequence":"additional","affiliation":[{"name":"Research Laboratory in Industrial Computing and Networks (RIIR), University of Oran 1, B.P. 1524, El M\u2019Naouer, Oran 31000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4778-0123","authenticated-orcid":false,"given":"Abou El Hassan","family":"Benyamina","sequence":"additional","affiliation":[{"name":"Laboratory of Parallel, Embedded Architectures and Intensive Computing (LAPECI), University of Oran 1, B.P. 1524, El M\u2019Naouer, Oran 31000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"ref_1","unstructured":"Mitra, A., Vangipuram, S.L.T., Bapatla, A.K., Bathalapalli, V.K.V.V., Mohanty, S.P., Kougianos, E., and Ray, C. 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