Precision Agriculture

, Volume 18, Issue 4, pp 635–666 | Cite as

A review of recent sensing technologies to detect invertebrates on crops

  • Huajian Liu
  • Sang-Heon Lee
  • Javaan Singh Chahl


Precision agriculture needs integrated pest management (IPM), for which detection and identification of target invertebrate species is a prerequisite. Researchers have been developing various technologies to detect pests more efficiently and accurately. However, these existing sensing technologies still have limitations for effective infield applications. This review paper aims to explore the relative technologies and find a sensing method that has potential to detect and identify common invertebrates on crops, such as butterflies, locusts, snails and slugs. It was found that there are two main research branches for invertebrate detection and identification: acoustic sensing and machine vision system (MVS). Acoustic sensing is suitable for detecting and identifying pests in soil, stored grains and wood, while usually acoustic sensors need to be attached to samples for inspection, which causes difficulties for efficient infield applications. MVS has the potential to provide a more effective and flexible way to detect and identify invertebrates on crops. In recent work with MVS, the technologies of invertebrate identification have been intensively studied, however, infield detection is relatively weak. This review points out the current research gaps and then discusses the potential research directions.


Invertebrate detection Insect identification IPM Machine vision Acoustic sensing 

Artificial neural network




Computerized tomographic


Digital automated identification system


Field of view


Gray-level co-occurrence matrix


Impact acoustic


Integrated pest management system


Linear discriminant analysis


Laser Doppler vibrometer


Light emitting diode


Logistic model tree


Modulus of elasticity


Machine vision system


Quadratic discriminant analysis


Structure from motion


Scale-invariant feature transform


Support vector machine


Time of flight




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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of EngineeringUniversity of South AustraliaAdelaideAustralia
  2. 2.Joint Operations and Analysis DivisionDefence Science and Technology OrganisationCanberraAustralia

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