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Abstract

Proximal sensing refers to measurements using sensors in close proximity to 
the object of interest (Adamchuk et al. 2018). Proximal crop sensors are used to 
collect information about a growing crop and can be mounted on the ground, 
handheld or borne by vehicles such as tractors or robots. Remote sensing 
involves the measurement of crop properties often with similar equipment as 
used in proximal sensing, but from a greater distance, using satellites, airplanes 
or unmanned aerial vehicles (UAVs, drones). The latter may also be used at 
short distances. At the other end of the spatial scale, there are sensors that 
can be used very close to, or in contact with, plant parts. Hence, there is a wide 
range of different types of sensors and scales on which they are used. Different 
sensors can also be used in combination, e.g. a proximal sensor can be used to 
calibrate data collected by remote sensing (Fig. 1).
In most cases, crop sensors collect inferential data, i.e. they do not directly 
measure crop properties of interest but rather produce a metric that can be 
used to estimate these properties. For example, light in different wavelengths 
reflected by a crop canopy can be recorded by a crop sensor and translated into 
useful information for agricultural management using established empirical 
relationships with e.g. the protein content of the crop.
Proximal crop sensors are used to assess and predict a range of different 
crop conditions, such as nutrient status, incidence of weeds and diseases, 
and drought stress in plants. The ripening stage of fruits and even number 
of spikes in a wheat stand can also be determined from digital images. Rapid 
technological development and access to artificial intelligence and machine 
learning methods have enabled new applications that were not possible just a 
few years ago, while still employing sensor techniques that have been used for 
a number of years.

Published in

Title: Precision agriculture for sustainability
Publisher: Burleigh Dodds

SLU Authors

UKÄ Subject classification

Agricultural Science
Earth Observation

Publication identifier

  • DOI: https://doi.org/10.19103/AS.2025.0152.02
  • ISBN: 9781801468817

Permanent link to this page (URI)

https://res.slu.se/id/publ/144604