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Machine Vision System for Orchard Management

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Machine Vision and Navigation

Abstract

This chapter overviews different machine vision systems in agricultural applications. Several different applications are presented, but a machine vision system which estimates fruit yield, an example of an orchard management application, is discussed at length. From the farmer’s perspective, an early yield prediction serves as an early revenue estimate. From this prediction, resources, such as employees and storage space, can more efficiently be allocated, and future seasons can be better planned. The yield estimate is accomplished using a camera with a color filter that isolates the blossoms on a tree when the tree is in its full blossom. The blossoms in the resulting image can be counted and the yield estimated. An estimate during the blossom period, as compared to when the fruit has begun to mature, provides a crop yield prediction several months in advance. Discussed as well, in this chapter, is a machine vision system which navigates a robot through orchard rows. This system can be used in conjunction with the yield estimation system, but it has additional applications such as incorporating a water or pesticide system, which can treat the trees as it passes by. To be effective, this type of system, must consider the operating scene as it can limit or constraint the system effectiveness. Such systems tend to be unique to the operating environment.

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

Cov:

Covariance

GPS:

Global Positioning System

IR:

Infrared

K I :

Integral gain

K P :

Proportional gain

LIDAR:

Light detection and ranging

NIR:

Near infrared

PI:

Proportional-plus-integral

RGB:

Red, Green, and Blue

RMS:

Root mean square

UAV:

Unmanned arial vehicle

UGV:

Unmanned ground vehicle

Var:

Variance

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Author information

Authors and Affiliations

  1. Department of Physics and Engineering, Northwest Nazarene University, Nampa, ID, USA

    Duke M. Bulanon, Tyler Hestand, Connor Nogales & Brice Allen

  2. Department of Mathematics and Computer Science, Northwest Nazarene University, Nampa, ID, USA

    Jason Colwell

Authors
  1. Duke M. Bulanon
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  2. Tyler Hestand
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  3. Connor Nogales
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  4. Brice Allen
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  5. Jason Colwell
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Corresponding author

Correspondence to Duke M. Bulanon .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Oleg Sergiyenko

  2. Universidad Autónoma de Baja California, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes

  3. Leuphana University of Lueneburg, Lueneburg, Low Saxony, Niedersachsen, Germany

    Paolo Mercorelli

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Bulanon, D.M., Hestand, T., Nogales, C., Allen, B., Colwell, J. (2020). Machine Vision System for Orchard Management. In: Sergiyenko, O., Flores-Fuentes, W., Mercorelli, P. (eds) Machine Vision and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-030-22587-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-22587-2_7

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