Chapter 7
Data Fusion in Agricultural Information
Systems
Berk Üstündağ
Abstract Data has an increasing role in agricultural production and management at
all scales depending on raising importance of yield efficiency and sustainability.
Remote sensing systems provide spatial information at some observation instants
and real-time terrestrial monitoring systems provide temporal information at some
observation points. Data fusion methods appear as feasible way of multi-temporal
mapping of information in Agricultural management. Data fusion uses computational models and machine learning methods on available spatial, temporal, and
multi-temporal data sets. In this chapter, basics of data indexing and segmentation in
Agricultural monitoring is given in accordance with application examples of timedelay neural networks, convolution and the wavelet transformation for data fusion.
Frequently used agro-meteorological indices and yield efficiency relationships are
also explained. Since many of the required monitoring parameters are usually not
feasible for real-time data acquisition, data fusion methods enable estimated parameters indirectly from the correlated set of available data. In contrary to distributed
characteristics of data resources and the users in agriculture, computational systems
have centralization trend through “Data as a Service” (DaaS), “Platform as a
Service” (PaaS) and “Artificial Intelligence as Service” (AIaaS). Data fusion is
especially expected to have an increasing role for large scale, continuous-time data
services in Agricultural applications.
Keywords Data fusion · Yield forecast · Agro-informatics · Convolutional neural
networks · Wavelet transformation · Deep learning · Evapotranspiration ·
Agricultural management · Time delay neural networks · Remote sensing
B. Üstündağ (*)
Computer and Informatics Engineering Faculty, Istanbul Technical University, Istanbul, Turkey
e-mail: bustundag@itu.edu.tr
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_7
103
Data Fusion in Agricultural Information
Systems
Berk Üstündağ
Abstract Data has an increasing role in agricultural production and management at
all scales depending on raising importance of yield efficiency and sustainability.
Remote sensing systems provide spatial information at some observation instants
and real-time terrestrial monitoring systems provide temporal information at some
observation points. Data fusion methods appear as feasible way of multi-temporal
mapping of information in Agricultural management. Data fusion uses computational models and machine learning methods on available spatial, temporal, and
multi-temporal data sets. In this chapter, basics of data indexing and segmentation in
Agricultural monitoring is given in accordance with application examples of timedelay neural networks, convolution and the wavelet transformation for data fusion.
Frequently used agro-meteorological indices and yield efficiency relationships are
also explained. Since many of the required monitoring parameters are usually not
feasible for real-time data acquisition, data fusion methods enable estimated parameters indirectly from the correlated set of available data. In contrary to distributed
characteristics of data resources and the users in agriculture, computational systems
have centralization trend through “Data as a Service” (DaaS), “Platform as a
Service” (PaaS) and “Artificial Intelligence as Service” (AIaaS). Data fusion is
especially expected to have an increasing role for large scale, continuous-time data
services in Agricultural applications.
Keywords Data fusion · Yield forecast · Agro-informatics · Convolutional neural
networks · Wavelet transformation · Deep learning · Evapotranspiration ·
Agricultural management · Time delay neural networks · Remote sensing
B. Üstündağ (*)
Computer and Informatics Engineering Faculty, Istanbul Technical University, Istanbul, Turkey
e-mail: bustundag@itu.edu.tr
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_7
103