Name
Abstract | ||
|---|---|---|
This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17030465 | SENSORS |
Keywords | Field | DocType |
wireless sensor networks,IoT,plant diseases and pests,grapevine,vineyards,environmental impact,ecologically sustainable vineyards,smart viticulture,precision agriculture | Vineyard,Decision support system,Vine,Precision agriculture,Electronic engineering,Mobile device,Software,Engineering,Wireless sensor network,Database,Environmental engineering,The Internet | Journal |
Volume | Issue | Citations |
17 | 3.0 | 10 |
PageRank | References | Authors |
0.56 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Josman P. Pérez-Expósito | 1 | 10 | 0.56 |
| Tiago M. Fernández-Caramés | 2 | 226 | 18.31 |
| Paula Fraga-Lamas | 3 | 241 | 19.01 |
| Luis Castedo | 4 | 577 | 73.24 |