dc.creator | Blanco-Carmona, Pedro | es |
dc.creator | Baeza Moreno, Lucía | es |
dc.creator | Hidalgo Fort, Eduardo | es |
dc.creator | Martín Clemente, Rubén | es |
dc.creator | González Carvajal, Ramón | es |
dc.creator | Muñoz Chavero, Fernando | es |
dc.date.accessioned | 2024-03-13T17:26:40Z | |
dc.date.available | 2024-03-13T17:26:40Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Blanco-Carmona, P., Baeza Moreno, L., Hidalgo Fort, E., Martín Clemente, R., González Carvajal, R. y Muñoz Chavero, F. (2023). AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats. Sensors, 23, 9733. https://doi.org/10.3390/s23249733. | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/156243 | |
dc.description | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | es |
dc.description.abstract | A significant proportion of the world’s agricultural production is lost to pests and diseases. To mitigate this problem, an AIoT system for the early detection of pest and disease risks in crops is proposed. It presents a system based on low-power and low-cost sensor nodes that collect environmental data and transmit it once a day to a server via a NB-IoT network. In addition, the sensor nodes use individual, retrainable and updatable machine learning algorithms to assess the risk level in the crop every 30 min. If a risk is detected, environmental data and the risk level are immediately sent. Additionally, the system enables two types of notification: email and flashing LED, providing online and offline risk notifications. As a result, the system was deployed in a real-world environment and the power consumption of the sensor nodes was characterized, validating their longevity and the correct functioning of the risk detection algorithms. This allows the farmer to know the status of their crop and to take early action to address these threats. | es |
dc.format | application/pdf | es |
dc.format.extent | 19 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 23, 9733. | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Internet of Things (IoT) | es |
dc.subject | Wireless Sensor Network (WSN) | es |
dc.subject | NB-IoT | es |
dc.subject | Smart agriculture | es |
dc.subject | Artificial Intelligence (AI) | es |
dc.title | AIoT in Agriculture: Safeguarding Crops from Pest and Disease Threats | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/23/24/9733 | es |
dc.identifier.doi | 10.3390/s23249733 | es |
dc.contributor.group | Universidad de Sevilla.TIC192: Ingeniería Electrónica | |
dc.contributor.group | Universidad de Sevilla. TIC203: Ingeniería Biomédica | |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 23 | es |
dc.publication.initialPage | 9733 | es |