Smart Water Management Platform IoT-Based Precision Irrigation for Agriculture
Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture
Carlos Kamienski, Juha-Pekka Soininen, Markus Taumberger, Ramide Dantas, Attilio Toscano, Tullio Salmon Cinotti, Rodrigo Filev Maia, André Torre Neto
The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished.
The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform.
Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.
Keywords: Internet of Things; smart water management; smart agriculture; precision irrigation; IoT platform; FIWARE; linked data
Annotate : This paper is an extended version of “SWAMP: an IoT-based Smart Water Management Platform for Precision Irrigation in Agriculture” presented at the 2018 Global Internet of Things Summit (GIoTS), Bilbao, Spain, 4–7 June 2018.
Kamienski C, Soininen J-P, Taumberger M, Dantas R, Toscano A, Salmon Cinotti T, Filev Maia R, Torre Neto A. Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture. Sensors . 2019; 19(2):276. DOI: 10.3390/s19020276