A. M. Mouazen, Thomas Alexandridis, Henning Buddenbaum, Yafit Cohen, Dimitrios Moshou, David Mulla, Said Nawar, Kenneth A. Sudduth

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


The first requirement for successful implementation of precision agriculture in the plant production sector is to measure and map within-field spatial and temporal variability. This can be achieved by means of two main sensing categories, namely, remote sensing and proximal sensing, for characterizing both soils and crops. Each of these two categories has advantageous and shortcomings. This chapter discusses the potential of different sensing technologies to characterize within-field variability of soils and crops, by providing high sampling resolution data necessary for site-specific management of farm input resources (e.g., fertilizers, water for irrigation, seeds and pesticides). Each of the sensing methods presented are discussed in terms of (1) a brief introduction of a technology, (2) list of properties and associated accuracy and practicality and (3) application case studies for agricultural management.

Original languageEnglish (US)
Title of host publicationAgricultural Internet of Things and Decision Support for Precision Smart Farming
PublisherElsevier Inc.
Number of pages104
ISBN (Electronic)9780128183731
ISBN (Print)9780128182356
StatePublished - Jan 14 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Inc. All rights reserved.


  • Crop
  • High sampling resolution
  • Proximal sensing
  • Remote sensing
  • Soil


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