A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development

Lacotte, Virginie; Dell’Aglio, Elisa; Peignier, Sergio; Benzaoui, Fadéla; Heddi, Abdelaziz; Rebollo, Rita; Silva, Pedro Da

doi:10.1016/j.heliyon.2023.e13962

Cited by 4 publications

(5 citation statements)

References 62 publications

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“…Their application in entomological research has also provided solutions to several problems that were previously unresolved or difficult to understand. Colours and colour patterns have been used to study a wide range of ecological and practical plant conservation topics, including colour changes caused by pests on food plants (Clement et al, 2015), to assess phenotypic peculiarities accompanying insect development (Lacotte et al, 2023), to provide data for prognostic tools (Hassan et al, 2014), or to understand different mechanisms of crypsis (Sherratt et al, 2007).…”

Section: Open Accessmentioning

confidence: 99%

Colour polymorphism of cotton bollworm larvae as a function of the type of host plant providing its development

Keszthelyi,

Orsi-Gibicsár,

Pál-Fám

et al. 2024

Front. Ecol. Evol.

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A well-known insect phenomenon is colour polymorphism, from which the species hopes to gain some ecological advantage. Herbivores adapt to the colours of their environment to reduce their chances of becoming prey, while predators use camouflage colours to succeed in hiding from prey. The larvae of the cosmopolitan, highly invasive cotton bollworm (Helicoverpa armigera Hbn., Lep.: Noctuidae) also benefit from this morphological polymorphism. Their fully-developed larvae can take on various colours when feeding on foliage. Our study aimed to determine the host plant-related colour adaptation of H. armigera larvae collected from different plant organs using different spectral analysis methods. Our studies, based on colour analysis of photographic images, showed that the colour of fully-developed larvae of the species is highly correlated with the colour of the damaged nutrient plant. The dominant colours of the larvae show a high similarity with the colours of the consumed host plant parts. The RGB-based analysis confirmed that larval colours are clustered according to the host plant’s organs and are mainly located in the yellow and green regions of the visible light spectrum. All these results confirm the extraordinary adaptive capabilities of this invasive species, which, among other things, verify its worldwide distribution.

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Section: Open Accessmentioning

confidence: 99%

Colour polymorphism of cotton bollworm larvae as a function of the type of host plant providing its development

Keszthelyi,

Orsi-Gibicsár,

Pál-Fám

et al. 2024

Front. Ecol. Evol.

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show abstract

“…To train such a classifier, we elaborated a one-class training dataset using principal component analysis (PCA) [45] and the K-means clustering algorithm [46]. These techniques are traditionally known to perform reliably for labeling and classification of hyperspectral images [30,33,36,47]. Thus, in order to capture each aphid's spectrum, given its small size with respect to the entire image, we focused on the aphid's surrounding area.…”

Section: Training Dataset Annotationmentioning

confidence: 99%

“…Digital color imaging is easy to use and provides copious amounts of information regarding pests, symptoms, and spatial distributions. Nevertheless, this technique has its limits as colors and shapes can become mixed up; its efficiency strongly depends on the image quality and the distance and orientation of the device [11,30]. One solution would be to couple spatial and spectral resolutions with spectral sensors such as hyperspectral imaging (HSI) systems [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, this technique has its limits as colors and shapes can become mixed up; its efficiency strongly depends on the image quality and the distance and orientation of the device [11,30]. One solution would be to couple spatial and spectral resolutions with spectral sensors such as hyperspectral imaging (HSI) systems [30][31][32]. A hyperspectral image is a hypercube composed of two spatial axes and one spectral axis at high resolution, providing a light spectrum with full wavelength information for each pixel of the image.…”

Section: Introductionmentioning

confidence: 99%

“…A hyperspectral image is a hypercube composed of two spatial axes and one spectral axis at high resolution, providing a light spectrum with full wavelength information for each pixel of the image. Thus, hyperspectral images contain a large amount of information (≈1 Gb) making it possible to analyze a complex environment with minor components using machine learning approaches suitable for this type of data [30]. In this context, previous studies successfully used HSI analysis to classify leaves and plants as non-infected or infected by various diseases [33,34] or aphids [32,35].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians

et al. 2023

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Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids’ hyperspectral images, which were captured in a controlled laboratory environment. This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a Precision of 0.97, a Recall of 0.91, an F1 score of 0.94, and an AUPR score of 0.98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field.

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Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis

Mahalakshmi,

Agarwal,

Kudari

et al. 2024

Lecture Notes in Electrical Engineering

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A comparative study revealed hyperspectral imaging as a potential standardized tool for the analysis of cuticle tanning over insect development

Cited by 4 publications

References 62 publications

Colour polymorphism of cotton bollworm larvae as a function of the type of host plant providing its development

Colour polymorphism of cotton bollworm larvae as a function of the type of host plant providing its development

Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians

Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis

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