Using remote and proximal sensor data in precision agriculture applications

Publication date: 20/02/2023
Extent: 26 pages

Contributions by: Luciano S. Shiratsuchi and Franciele M. Carneiro, Louisiana State University, USA; Francielle M. Ferreira, São Paulo State University (UNESP), Brazil; Phillip Lanza and Fagner A. Rontani, Louisiana State University, USA; Armando L. Brito Filho, São Paulo State University (UNESP), Brazil; Getúlio F. Seben Junior, State University of Mato Grosso (UNEMAT), Brazil; Ziany N. Brandao, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil; Carlos A. Silva Junior, State University of Mato Grosso (UNEMAT), Brazil; Paulo E. Teodoro, Federal University of Mato Grosso do Sul (UFMS), Brazil; and Syam Dodla, Louisiana State University, USA

Chapter synopsis:

This chapter reviews key issues in using sensor data in precision agriculture (PA) and, in particular, their mode of deployment (proximal or remote). It assesses relative strengths and weaknesses of proximal sensing techniques, compared with imaging data typically acquired from remote sensing platforms, before assessing trade-offs in sensor data resolution, as well as sources of error in the way data is processed. The chapter concludes by looking at ways of integrating remote and proximal sensor data, to utilize the beneficial characteristics of each type of data to improve the impact precision agriculture in improving efficiency and sustainability.

DOI: 10.19103/AS.2022.0107.19