Name
Title | ||
|---|---|---|
Collaborative Data Mining in Agriculture for Prediction of Soil Moisture and Temperature |
Abstract | ||
|---|---|---|
Climate change affects agriculture in many ways. Reducing the vulnerability of agricultural systems to climate change and enhancing their capacity to adapt would generate better results with fewer losses. Under the conditions, according to The United Nations Food and Agriculture Organization, the world has to produce 70% more food in 2050 than it produced in 2006, to feed the growing population, it is obvious that any innovative ideas that help agriculture are optimal and needed. An option for increasing efficiency of agriculture is a data mining process that can predict climate conditions and humidity of soil. Determining the optimal time for planting and harvesting could be based on predictions from a data mining process. In this scenario, the application of collaborative data mining techniques, would offer solution for the cases in which one sources do not poses useful data for mining, and the process uses date from another sources correlated. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19807-7_15 | SOFTWARE ENGINEERING METHODS IN INTELLIGENT ALGORITHMS, VOL 1 |
Keywords | DocType | Volume |
Machine learning,Data mining,Collaborative data mining,Soil moisture prediction,Prediction in agriculture | Conference | 984 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carmen Ana Anton | 1 | 0 | 0.34 |
| Oliviu Matei | 2 | 43 | 11.15 |
| Anca Avram | 3 | 1 | 1.03 |