Precision Agriculture

, Volume 18, Issue 4, pp 667–683 | Cite as

An evaluation of the contribution of ultraviolet in fused multispectral images for invertebrate detection on green leaves

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


Real-time detection and identification of invertebrates on crops is a useful capability for integrated pest management, however, this challenging task has not been solved. Compared with other technologies, a machine vision system (MVS) could provide a more flexible solution. To date, most studies have focused on counting and identifying specimens in sample containers, glass slides or traps where the illumination and background reflection can be well controlled; few studies have been conducted to detect pests on plants. In the context of invertebrate detection or identification, the spectra of visible light, near infrared (NIR) and soft X-ray have been well studied, while the spectrum of ultraviolet (UV) is still untouched. Many species of bird prey on invertebrate pests and have adaptations in their visual system to enhance detection of targets. These birds can use both UV and visible light to hunt. If the mechanisms of bird vision could be transferred to a technological visual system, it might improve the capability for invertebrate detection. This study provides an initial estimation of the contribution of UV for invertebrate detection on green leaves. By fusing the UV images into the visible light and NIR images, the MVS can detect nine invertebrate species on leaves of plants and the UV images can significantly reduce segmentation errors. The initial experiment was conducted in a laboratory, however, this study shows promise for infield applications.


Invertebrate detection Insect identification Machine vision system Image fusion IPM 



We thank Dr. Michael Nash and Dr. Greg Baker in South Australian Research and Development Institute (SARDI) for providing the invertebrate specimens for study.


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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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