Identification of species and geographical strains of<i>Sitophilus oryzae</i>and<i>Sitophilus zeamais</i>using the visible/near-infrared hyperspectral imaging technique

Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Shuai, Qi; Lin, Tong; Ren, Yonglin

doi:10.1002/ps.3893

Cited by 27 publications

(54 citation statements)

References 28 publications

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“…In addition to a low cost sensor, there is a requirement to implement the homogenous and diffuse (shadow free) optical illumination platform, such as reported elsewhere [27], in order to utilise the more subtle species classifier models of the Phase-2 study within the actively illuminated imaging sensor system architecture of the Phase-1 investigation. Furthermore, to enhance the usability of the system compensation is required for the orientation and distance of the focal plane of the sample with respect to the imaging array lens, across the multiple wavelengths.…”

Section: Discussionmentioning

confidence: 99%

A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor

et al. 2018

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BackgroundThe paper introduces a multispectral imaging system and data-processing approach for the identification and discrimination of morphologically indistinguishable cryptic species of the destructive crop pest, the whitefly Bemisia tabaci. This investigation and the corresponding system design, was undertaken in two phases under controlled laboratory conditions. The first exploited a prototype benchtop variant of the proposed sensor system to analyse four cryptic species of whitefly reared under similar conditions. The second phase, of the methodology development, employed a commercial high-precision laboratory hyperspectral imager to recover reference data from five cryptic species of whitefly, immobilized through flash freezing, and taken from across four feeding environments.ResultsThe initial results, for the single feeding environment, showed that a correct species classification could be achieved in 85–95% of cases, utilising linear Partial Least Squares approaches. The robustness of the classification approach was then extended both in terms of the automated spatial extraction of the most pertinent insect body parts, to assist with the spectral classification model, as well as the incorporation of a non-linear Support Vector Classifier to maintain the overall classification accuracy at 88–98%, irrespective of the feeding and crop environment.ConclusionThis study demonstrates that through an integration of both the spatial data, associated with the multispectral images being used to separate different regions of the insect, and subsequent spectral analysis of those sub-regions, that B. tabaci viral vectors can be differentiated from other cryptic species, that appear morphologically indistinguishable to a human observer, with an accuracy of up to 98%. The implications for the engineering design for an in-field, handheld, sensor system is discussed with respect to the learning gained from this initial stage of the methodology development.

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Section: Discussionmentioning

confidence: 99%

A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor

et al. 2018

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“…In a study of two closely related stored grain species ( Sitophilus spp .) and with specimens collected from different regions in China, Cao, Zhang [36] examined hyperspectral reflectance features and identified spectral bands at 505, 659 and 955 nm as contributing the most to their classification algorithms. From this brief review, it appears that detection of spectral ranges associated with strong responses to origin of specimens, biotypes, and species vary considerably among clades of organisms.…”

Section: Discussionmentioning

confidence: 99%

Using proximal remote sensing in non-invasive phenotyping of invertebrates

Feng

et al. 2017

PLoS ONE

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Proximal imaging remote sensing technologies are used to phenotype and to characterize organisms based on specific external body reflectance features. These imaging technologies are gaining interest and becoming more widely used and applied in ecological, systematic, evolutionary, and physiological studies of plants and also of animals. However, important factors may impact the quality and consistency of body reflectance features and therefore the ability to use these technologies as part of non-invasive phenotyping and characterization of organisms. We acquired hyperspectral body reflectance profiles from three insect species, and we examined how preparation procedures and preservation time affected the ability to detect reflectance responses to gender, origin, and age. Different portions of the radiometric spectrum varied markedly in their sensitivity to preparation procedures and preservation time. Based on studies of three insect species, we successfully identified specific radiometric regions, in which phenotypic traits become significantly more pronounced based on either: 1) gentle cleaning of museum specimens with distilled water, or 2) killing and preserving insect specimens in 70% ethanol. Standardization of killing and preservation procedures will greatly increase the ability to use proximal imaging remote sensing technologies as part of phenotyping and also when used in ecological and evolutionary studies of invertebrates.

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“…Near‐infrared (NIR) spectroscopy has also proved a powerful tool for application in pest management, for example in the discrimination of insect species, in assessing the age of insects or in the detection of insects concealed in grain or pupae . Conventional imaging is deemed insufficient for the analysis of samples of similar shape and colour, and may require additional information on the chemical background of the samples for higher accuracy . NIR spectroscopy can provide chemical information on a sample; however, it is a point‐based measurement and is compromised by the spatial heterogeneity present in a sample, which may reduce accuracy .…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of the MS imaging system are that it is non‐destructive and requires minimal or no prior sample preparation . A recent study on the identification of rice weevil species, Sitophilus (Coleoptera: Curculionidae), used an MS imaging approach by selecting only images of the useful wavelengths from an originally visible/NIR hyperspectral image . A similar approach was used to study pupal development in the tsetse fly ( Glossina palpalis gambiensis ) (Diptera: Glossinidae) using NIR hyperspectral imaging .…”

Section: Introductionmentioning

confidence: 99%

“…A similar approach was used to study pupal development in the tsetse fly ( Glossina palpalis gambiensis ) (Diptera: Glossinidae) using NIR hyperspectral imaging . These studies involving hyperspectral imaging have highlighted the MS imaging system as a prospective technology with practical application, primarily due to a significant reduction in image size and rapid analysis . The potential of the MS imaging system to evaluate seed viability, for varietal identification of seeds or to detect adulteration in meat has already been demonstrated .…”

Section: Introductionmentioning

confidence: 99%

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Detection of live larvae in cocoons of Bathyplectes curculionis (Hymenoptera: Ichneumonidae) using visible/near‐infrared multispectral imaging

Shrestha

Topbjerg

Ytting

et al. 2018

Pest Management Science

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Identification of species and geographical strains ofSitophilus oryzaeandSitophilus zeamaisusing the visible/near-infrared hyperspectral imaging technique

Cited by 27 publications

References 28 publications

A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor

A method for real-time classification of insect vectors of mosaic and brown streak disease in cassava plants for future implementation within a low-cost, handheld, in-field multispectral imaging sensor

Using proximal remote sensing in non-invasive phenotyping of invertebrates

Detection of live larvae in cocoons of Bathyplectes curculionis (Hymenoptera: Ichneumonidae) using visible/near‐infrared multispectral imaging

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