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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
Article

Abstract

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.

Keywords

Invertebrate detection Insect identification IPM Machine vision Acoustic sensing 
ANN

Artificial neural network

BOR

Bag-of-region

CT

Computerized tomographic

DAISY

Digital automated identification system

FOV

Field of view

GLCM

Gray-level co-occurrence matrix

IA

Impact acoustic

IPM

Integrated pest management system

LDA

Linear discriminant analysis

LDV

Laser Doppler vibrometer

LED

Light emitting diode

LMT

Logistic model tree

MOE

Modulus of elasticity

MVS

Machine vision system

QDA

Quadratic discriminant analysis

SFM

Structure from motion

SIFT

Scale-invariant feature transform

SVM

Support vector machine

TOF

Time of flight

UV

Ultraviolet

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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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