dc.creator | Shafi, Uferah | es |
dc.creator | Mumtaz, Rafia | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Hassan, Syed Ali | es |
dc.creator | Raza Zaidi, Syed Ali | es |
dc.creator | Iqbal, Naveed | es |
dc.date.accessioned | 2021-05-12T09:17:24Z | |
dc.date.available | 2021-05-12T09:17:24Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Shafi, U., Mumtaz, R., García Nieto, J.M., Hassan, S.A., Raza Zaidi, S.A. y Iqbal, N. (2019). Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors, 19 (17), 3796-1-3796-25. | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108911 | |
dc.description.abstract | Internet of Things (IoT)-based automation of agricultural events can change the agriculture
sector from being static and manual to dynamic and smart, leading to enhanced production with
reduced human efforts. Precision Agriculture (PA) along withWireless Sensor Network (WSN) are the
main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure
that the crops receive exactly what they need to optimize productivity and sustainability. PA includes
retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the
fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned
or unmanned), which are further processed to extract information used to provide future decisions.
In this paper, a review of near and remote sensor networks in the agriculture domain is presented
along with several considerations and challenges. This survey includes wireless communication
technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms
used to obtain spectral images of crops, the common vegetation indices used to analyse spectral
images and applications of WSN in agriculture. As a proof of concept, we present a case study
showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution
for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor
network-based system to monitor real-time crop health status. The second module uses a low altitude
remote sensing platform to obtain multi-spectral imagery, which is further processed to classify
healthy and unhealthy crops. We also highlight the results obtained using a case study and list the
challenges and future directions based on our work. | es |
dc.format | application/pdf | es |
dc.format.extent | 25 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 19 (17), 3796-1-3796-25. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Smart agriculture | es |
dc.subject | Precision agriculture | es |
dc.subject | Vegetation index | es |
dc.subject | Internet of Things | es |
dc.title | Precision Agriculture Techniques and Practices: From Considerations to Applications | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/19/17/3796 | es |
dc.identifier.doi | 10.3390/s19173796 | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 19 | es |
dc.publication.issue | 17 | es |
dc.publication.initialPage | 3796-1 | es |
dc.publication.endPage | 3796-25 | es |