DMA 2013 - International Workshop Data Mining in Agriculture, DMA 2013

Data mining, the art and science of intelligent analysis of (usually large) data sets for meaningful (and previously unknown) insights, is nowadays actively applied in a wide range of disciplines related to agriculture. Due to the emerging importance of data mining techniques and methodologies in the area of agriculture, this workshop aims to bring together practitioners and researchers in this field. It creates a community of people who are actively using data mining tools and techniques and apply them to agriculture data.
Scope of the Workshop
Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population. A key to this is the usage of modern technologies such as GPS (for precision agriculture) and data mining techniques to take advantage of the soil's heterogeneity. The large amounts of data that are nowadays virtually harvested along with the crops have to be analysed and should be used to their full extent - this is clearly a data mining task. Data mining allows to extract the most important information from such vast data and to uncover previously unknown patterns that may be relevant to current agricultural problems, thereby helping farmers and managing organisations to transform data into business decisions.
The goals of this workshop are to provide a forum for identifying important contributions and opportunities for research on data mining as it applies to agriculture to promote the systematic study of how to apply data mining to agriculture data to develop practical applications.
Topics of interest include (but are not limited to):
Data Mining on Sensor and Spatial Data from Agricultural Applications
Analysis of Remote Sensor Data
Feature Selection on Agricultural Data
Evaluation of Data Mining Experiments
Spatial Autocorrelation in Agricultural Data
Target Group
Computer scientists working on agriculture problems
Machine learners, data miners and statisticians with an interest in agriculture
Agricultural researchers with a background in data mining
Agriculture professionals with data mining applications and showcases
Middleware developers covering data mining aspects in data (pre)processing
Farmers with an interest in data mining and related aspects
... and similar professional and research people