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Advances in proximal sensor fusion and multi-sensor platforms for improved crop management

Code: 9781801465540
Publication date: 20/02/2023
Extent: 20 pages

Contributions by: David W. Franzen and Anne M. Denton, North Dakota State University, USA

Chapter synopsis:

Numerous studies referred to in this book and in other publications have examined the relationship between a proximal sensor, or remote sensing with crop yield and other attributes of interest to precision agriculture. Combining data from two or more sensors tends to increase the relationship between sensor readings and crop attributes. Crop canopy height, active-optical sensor readings, remote sensing data, and weather data have been combined to increase predictability of crop attributes. The fusion of these data requires the use of appropriate statistical tools. Description of several of these tools is provided in this chapter.



DOI: 10.19103/AS.2022.0107.18

Open Access

This is an open access chapter distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY).

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Table of contents
  • 1 Introduction
  • 2 Use of plant height and proximal/remote sensing
  • 3 Sensors and weather data
  • 4 Multi-sensor approaches
  • 5 Statistical tools for fusing multi-sensor data
  • 6 Conclusion and future trends
  • 7 Where to look for further information
  • 8 References

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