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FIGURE 6 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 6. Phylogeny of Lepidosaphes Shimer and allies (Diaspidinae: Lepidosaphidini). This figure shows the phylogeny of the clade labeled "Lepidosaphes + Australian Lepidosaphidini" in Fig. 4. We have condensed the figure somewhat by representing 3 well-sampled species with triangular placeholders rather than listing all the individuals sampled. For further explanation of the analysis and notation, see the caption to Fig. 1.

FIGURE 18 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 18. Hendersonaspis anomala (Green), second-instar male, illustrated from type material. The dorsal surface is shown on the left and the ventral surface on the right. The small figures on the margins show enlarged images of an antenna, a small thoracic ventral duct, and a larger abdominal dorsal duct.

FIGURE 15 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 15. Phylogeny of the tribe Parlatoriini (Aspidiotinae), whose relationship to other Aspidiotinae is shown in Fig. 3. For further explanation of the analysis and notation, see the caption to Fig. 1.

FIGURE 10 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 10. Phylogeny of Australasian Fioriniina species (Diaspidinae: Diaspidini: Fioriniina). This figure shows the phylogeny of the clade labeled "Australasian Fioriniina" in Fig. 8. It includes the great majority of Australasian Fioriniina species, but note that it does not include Pellucidaspis, which apparently colonized Australasia independently (Fig. 8). For further explanation of the analysis and notation, see the caption to Fig. 1.

FIGURE 12 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 12. Phylogeny of Chionaspis Signoret and allies (Diaspidinae: Diaspidini: Chionaspidina). This figure shows the phylogeny of the clade labeled "Chionaspis and allies" in Fig. 11. To save space, a triangular placeholder labelled "North American pine-feeding Chionaspis" represents 70 individuals of North American pine-feeding species, including C. pinifoliae (Fitch), C. heterophyllae (Cooley), C. brachycephalon Vea, C. sonorae Vea, and C. torreyanae Vea. A more complete analysis and discussion of this clade can be found in Gwiazdowski et al. (2011) and Vea et al. (2013). For further explanation of the analysis and notation used here, see the caption to Fig. 1.

FIGURE 13 in Phylogeny and classification of armored scale insects (Hemiptera: Coccomorpha: Diaspididae)

FIGURE 13. Phylogeny of the tribe Leucaspidini (Aspidiotinae), whose relationship to other Aspidiotinae is shown in Fig. 3. UG4409 is an undescribed genus. For further explanation of the analysis and notation, see the caption to Fig. 1.

opencc-by-4.0Nov 2019View details →

Image data used for publication "Species-level image classification with convolutional neural network enable insect identification from habitus images "

Image-crops of specimens from insect drawers In 2017 we scanned 208 insect drawers containing the collection of british carabids from the Natural History Museum London and extracted crops from the scanned images. This database contain 63.364 specimens that we used to train, validate and test a convolutional neural network. Each folder is named as the gbif id number. E.g. Carabus problematicus is 4470555: https://www.gbif.org/species/4470555

opencc-by-4.0Sep 2024View details →

Insect Classification

The data utilized in our experiments was obtained from the Barcode of Life Data System (BOLD), which is a cloud-based data storage and analysis platform developed at the Centre for Biodiversity Genomics in Canada. The data consisted of 32424 image samples of insect species from four Insecta orders, Diptera, Coleoptera, Lepidoptera and Hymenoptera, each associated with a DNA barcode sequence of that specie.   Description of the .mat: # Insect Dataset * all_images: vector containing the 32424 64x64x3 images (RGB) pre normalized of the insects * all_dnas: vector containing the 32424 DNA barcodes in one-hot encoding 658x5 * all_labels: vector containing the species label for the corresponding DNA and image * all_boldids: vector of strings containing the id from boldsystemsv3 (https://v3.boldsystems.org/) they can be used to download from boldsystems the original DNA barcodes and the full size images and other data related to the sample * train_loc: indices of the training samples in all_dnas, all_images, all_labels, all_boldids * val_seen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species * val_unseen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species * test_seen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species * test_unseen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species * species2genus: the vector contains at index i the genus label of species with label i (e.g. species i has genus species2genus[i]) * described_species_labels_train: vector containing the labels of species that appear in the training set * described_species_labels_trainval: vector containing the labels of species that appear in the training set and/or the validation set * all_dna_features_cnn_original: vector of features extractedfrom DNA nucleotides with the method of Badirli, S., Picard, C. J., Mohler, G.,Richert, F., Akata, Z., & Dundar, M. (2023). Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution, 14, 1515-1530. https://doi.org/10.1111/2041-210X.14104 * all_image_features_resnet: vector of features extracted from the insect images with the method of the same paper as the all_dna_features_cnn_original with a pretrained resnet101 * all_dna_features_cnn_new: vector of features extracted from DNA nucleotides with our CNN * all_image_features_gan: vector of features extracted from the insect images with out method using a ReACGAN Note: all arrays and locs are 1-indexed like in MATLAB

FIGURE 6 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 6. Phylogram resulting from maximum likelihood analysis of 18S rDNA data using a GTR + G + I model.

FIGURE 5 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 5. Phylogram resulting from analysis of 18S rDNA: one of two trees resulting from Parsimony analysis of data, numbers on branches indicate bootstrap proportions.

FIGURE 1 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 1. Thysanoptera family relationships (Mound, Heming & Palmer, 1980) [This included the Melanthripidae within the Aeolothripidae].

opencc-by-nc-sa-4.0Sep 2023View details →

Insect Detect - insect classification dataset v2

The Insect Detect - insect classification dataset v2 contains mainly images of various insects sitting on or flying above an artificial flower platform. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect</a> DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification">first</a> dataset version were transferred to this dataset.This dataset is also available on <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification_v2">Roboflow Universe</a>. The images in the dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use this uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided <a href="https://colab.research.google.com/github/maxsitt/insect-detect-ml/blob/main/notebooks/YOLOv5_classification_training.ipynb">training notebook</a>. <h2>Classes</h2>This dataset contains the following 27 classes:<ul><li>ant (<a href="https://en.wikipedia.org/wiki/Ant">Formicidae</a>)</li><li>bee (<a href="https://en.wikipedia.org/wiki/Bee">Anthophila</a> excluding Apis mellifera and Bombus sp.)</li><li>bee_apis (<a href="https://en.wikipedia.org/wiki/Western_honey_bee">Apis mellifera</a>)</li><li>bee_bombus (<a href="https://en.wikipedia.org/wiki/Bumblebee">Bombus sp.</a>)</li><li>beetle (<a href="https://en.wikipedia.org/wiki/Beetle">Coleoptera</a> excluding Coccinellidae and some Oedemeridae)</li><li>beetle_cocci (<a href="https://en.wikipedia.org/wiki/Coccinellidae">Coccinellidae</a>)</li><li>beetle_oedem (visually distinct <a href="https://en.wikipedia.org/wiki/Oedemeridae">Oedemeridae</a>)</li><li>bug (<a href="https://en.wikipedia.org/wiki/Heteroptera">Heteroptera</a> excluding Graphosoma italicum)</li><li>bug_grapho (<a href="https://en.wikipedia.org/wiki/Graphosoma_italicum">Graphosoma italicum</a>)</li><li>fly (<a href="https://en.wikipedia.org/wiki/Brachycera">Brachycera</a> excluding Empididae, Sarcophagidae, Syrphidae and small Brachycera)</li><li>fly_empi (<a href="https://en.wikipedia.org/wiki/Empididae">Empididae</a>)</li><li>fly_sarco (visually distinct <a href="https://en.wikipedia.org/wiki/Flesh_fly">Sarcophagidae</a>)</li><li>fly_small (small <a href="https://en.wikipedia.org/wiki/Brachycera">Brachycera</a>)</li><li>hfly_episyr (hoverfly <a href="https://en.wikipedia.org/wiki/Episyrphus_balteatus">Episyrphus balteatus</a>)</li><li>hfly_eristal (hoverfly <a href="https://en.wikipedia.org/wiki/Eristalis">Eristalis</a> sp., mainly <a href="https://en.wikipedia.org/wiki/Eristalis_tenax">Eristalis tenax</a>)</li><li>hfly_eupeo (mainly hoverfly <a href="https://en.wikipedia.org/wiki/Eupeodes_corollae">Eupeodes corollae</a> and <a href="https://en.wikipedia.org/wiki/Scaeva_pyrastri">Scaeva pyrastri</a>)</li><li>hfly_myathr (hoverfly <a href="https://en.wikipedia.org/wiki/Myathropa_florea">Myathropa florea</a>)</li><li>hfly_sphaero (hoverfly <a href="https://en.wikipedia.org/wiki/Sphaerophoria">Sphaerophoria sp.</a>, mainly <a href="https://en.wikipedia.org/wiki/Sphaerophoria_scripta">Sphaerophoria scripta</a>)</li><li>hfly_syrphus (mainly hoverfly <a href="https://en.wikipedia.org/wiki/Syrphus">Syrphus sp.</a>)</li><li>lepi (<a href="https://en.wikipedia.org/wiki/Lepidoptera">Lepidoptera</a>)</li><li>none_bg (images with no insect - background (platform))</li><li>none_bird (images with no insect - bird sitting on platform)</li><li>none_dirt (images with no insect - leaves and other plant material, bird droppings)</li><li>none_shadow (images with no insect - shadows of insects or surrounding plants)</li><li>other (other Arthropods, including various <a href="https://en.wikipedia.org/wiki/Hymenoptera">Hymenoptera</a> and <a href="https://en.wikipedia.org/wiki/Sawfly">Symphyta</a>, <a href="https://en.wikipedia.org/wiki/Fly">Diptera</a>, <a href="https://en.wikipedia.org/wiki/Orthoptera">Orthoptera</a>, <a href="https://en.wikipedia.org/wiki/Auchenorrhyncha">Auchenorrhyncha</a>, <a href="https://en.wikipedia.org/wiki/Neuroptera">Neuroptera</a>, <a href="https://en.wikipedia.org/wiki/Spider">Araneae</a>)</li><li>scorpionfly (<a href="https://en.wikipedia.org/wiki/Panorpa">Panorpa sp.</a>)</li><li>wasp (mainly <a href="https://en.wikipedia.org/wiki/Vespula">Vespula sp.</a> and <a href="https://en.wikipedia.org/wiki/European_paper_wasp">Polistes dominula</a>)</li></ul>For the classes hfly_eupeo and hfly_syrphus a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that are important for a classification into one of these classes, some specimens of Syrphus sp. might look more like Eupeodes sp. and vice versa.The images were sorted to the respective class by considering taxonomic and visual distinctions. However, this dataset is still rather small regarding the visually extremely diverse <a href="https://en.wikipedia.org/wiki/Insect">Insecta</a>. Insects that are not included in this dataset can therefore be classified to the wrong class. All results should always be manually validated and false classifications can be used to extend this basic dataset and <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_classification/">retrain</a> your custom classification model. <h2>Deployment</h2>You can use this dataset as starting point to train your own insect classification models with the provided Google Colab <a href="https://colab.research.google.com/github/maxsitt/insect-detect-ml/blob/main/notebooks/YOLOv5_classification_training.ipynb">training notebook</a>. Read the model training <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_classification/">instructions</a> for more information.A insect classification model trained on this dataset is available in the <a href="https://github.com/maxsitt/insect-detect-ml#classification-model">insect-detect-ml</a> GitHub repo. To deploy the model on your PC (ONNX format for fast CPU inference), follow the provided step-by-step <a href="https://maxsitt.github.io/insect-detect-docs/deployment/classification/">instructions</a>. <h3>License</h3>This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>).

dryad32/100

Data from: Rapid and accurate taxonomic classification of insect (Class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier

Open the record for dataset details and reuse information.

publicFeb 2014View details →

dryad28/100

Data from: The insect-focused classification of fruit syndromes in tropical rainforests: an inter-continental comparison

We propose a new classification of rainforest plants into eight fruit syndromes, based on fruit morphology and other traits relevant to fruit-feeding insects. This classification is compared with other systems based on plant morphology or traits relevant to vertebrate fruit dispersers. Our syndromes are based on fruits sampled from 1,192 plant species at three Forest Global Earth Observatory plots: Barro Colorado Island (Panama), Khao Chong (Thailand) and Wanang (Papua New Guinea). The three plots differed widely in fruit syndrome composition. Plant species with fleshy, indehiscent fruits containing multiple seeds were important at all three sites. However, in Panama a high proportion of species had dry fruits, while in New Guinea and Thailand, species with fleshy drupes and thin mesocarps were dominant. Species with dry, winged seeds that do not develop as capsules were important in Thailand, reflecting the local importance of Dipterocarpaceae. These differences can also determine differences among frugivorous insect communities. Fruit syndromes and colours were phylogenetically flexible traits at the scale studied, as only three of the eight seed syndromes, and one of the 10 colours, showed significant phylogenetic clustering at either genus or family levels. Plant phylogeny was, however, the most important factor explaining differences in overall fruit syndrome composition among individual plant families or genera across the three study sites.

opencc-zeroDec 2017View details →

FIGURE 3 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 3. Relationships between some genera of Aeolothripidae (Marullo & Mound, 1995).

FIGURE 2 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 2. Tubulifera familiy relationships proposed by Bhatti (1994).

FIGURE 4 in The insect Order Thysanoptera: Classification versus Systematics*

FIGURE 4. Relationships between the smaller families of Terebrantia (Marullo & Mound, 2001).