Data Fusion and Its Applications in Agriculture

Dimitrios E. Moshou () and Xanthoula Eirini Pantazi ()
Additional contact information
Dimitrios E. Moshou: Aristotle University of Thessaloniki, School of Agriculture
Xanthoula Eirini Pantazi: Aristotle University of Thessaloniki, School of Agriculture

A chapter in Information and Communication Technologies for Agriculture—Theme II: Data, 2022, pp 17-40 from Springer

Abstract: Abstract An information revolution is currently occurring in agriculture resulting in the production of massive datasets at different spatial and temporal scales; therefore, efficient techniques for processing and summarizing data will be crucial for effective precision management. With the profusion and wide diversification of data sources provided by modern technology, such as remote and proximal sensing, sensor datasets could be used as auxiliary information to supplement a sparsely sampled target variable. Remote and proximal sensing data are often massive, taken on different spatial and temporal scales, and subject to measurement error biases. Moreover, differences between the instruments are always present; nevertheless, a data fusion approach could take advantage of their complementary features by combining the sensor datasets in a manner that is statistically robust. It would then be ideal to jointly use (fuse) partial information from the diverse today-available sources so efficiently to achieve a more comprehensive view and knowledge of the processes under study. The chapter investigates the data fusion process in agriculture and its connection to artificial intelligence, neural networks, and IoT in agriculture, and introduces the concepts of data fusion with applications in Remote and Proximal sensing.

Keywords: Information fusion; Context awareness; Artificial intelligence; Neural networks; Deep learning (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84148-5_2

Ordering information: This item can be ordered from
http://www.springer.com/9783030841485

DOI: 10.1007/978-3-030-84148-5_2

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().