Next Article in Journal
Changes in Root Anatomy of Peanut (Arachis hypogaea L.) under Different Durations of Early Season Drought
Previous Article in Journal
Effect of Salinity and Water Stress on the Essential Oil Components of Rosemary (Rosmarinus officinalis L.)
Open AccessArticle

A Smart Decision System for Digital Farming

1
Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain
2
Department of Signal Theory, Telematics and Communications, Universidad de Granada, 18071 Granada, Spain
3
Instituto de Investigación para la Gestión Integrada de zonas Costeras, Universitat Politècnica de València, 46730 Valencia, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(5), 216; https://doi.org/10.3390/agronomy9050216
Received: 26 January 2019 / Revised: 21 April 2019 / Accepted: 23 April 2019 / Published: 27 April 2019
New technologies have the potential to transform agriculture and to reduce environmental impact through a green revolution. Internet of Things (IoT)-based application development platforms have the potential to run farm management tools capable of monitoring real-time events when integrated into interactive innovation models for fertirrigation. Their capabilities must extend to flexible reconfiguration of programmed actions. IoT platforms require complex smart decision-making systems based on data-analysis and data mining of big data sets. In this paper, the advantages are demonstrated of a powerful tool that applies real-time decisions from data such as variable rate irrigation, and selected parameters from field and weather conditions. The field parameters, the index vegetation (estimated using aerial images), and the irrigation events, such as flow level, pressure level, and wind speed, are periodically sampled. Data is processed in a decision-making system based on learning prediction rules in conjunction with the Drools rule engine. The multimedia platform can be remotely controlled, and offers a smart farming open data network with shared restriction levels for information exchange oriented to farmers, the fertilizer provider, and agricultural technicians that should provide the farmer with added value in the form of better decision making or more efficient exploitation operations and management. View Full-Text
Keywords: smart farming; IoT farming; agriculture smart system; WSN agriculture; digital farming smart farming; IoT farming; agriculture smart system; WSN agriculture; digital farming
Show Figures

Figure 1

MDPI and ACS Style

Cambra Baseca, C.; Sendra, S.; Lloret, J.; Tomas, J. A Smart Decision System for Digital Farming. Agronomy 2019, 9, 216. https://doi.org/10.3390/agronomy9050216

AMA Style

Cambra Baseca C, Sendra S, Lloret J, Tomas J. A Smart Decision System for Digital Farming. Agronomy. 2019; 9(5):216. https://doi.org/10.3390/agronomy9050216

Chicago/Turabian Style

Cambra Baseca, Carlos; Sendra, Sandra; Lloret, Jaime; Tomas, Jesus. 2019. "A Smart Decision System for Digital Farming" Agronomy 9, no. 5: 216. https://doi.org/10.3390/agronomy9050216

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop