Data Mining and Pattern Recognition in Agriculture

被引：20
作者：
Bauckhage C. [1 ,3 ]
Kersting K. [2 ,3 ]
机构：
[1] B-IT, University of Bonn, Bonn
[2] IGG, University of Bonn, Bonn
[3] Fraunhofer IAIS, Sankt Augustin
来源：
Bauckhage, Christian (christian.bauckhage@iais.fraunhofer.de) | 1600年 / Springer Science and Business Media Deutschland GmbH卷 / 27期
关键词：
Cercospora Leaf Spot; Drought Stress; Leaf Spot; Local Binary Pattern; Precision Farming;
D O I：
10.1007/s13218-013-0273-0
中图分类号：
学科分类号：
摘要：
Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture. © 2013, Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：313 / 324
页数：11
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