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.
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Artificial neural network
Digital automated identification system
Field of view
Gray-level co-occurrence matrix
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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Liu, H., Lee, SH. & Chahl, J.S. A review of recent sensing technologies to detect invertebrates on crops. Precision Agric 18, 635–666 (2017). https://doi.org/10.1007/s11119-016-9473-6
- Invertebrate detection
- Insect identification
- Machine vision
- Acoustic sensing