Next generation insect taxonomic classification by comparing different deep learning algorithms

Ong, Song‐Quan; Hamid, Suhaila Abdul

doi:10.1371/journal.pone.0279094

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The experimental results showed that all of the three methods had high accuracy, the accuracy of Inception V3 reached 98.69%, that of the VGG16_bn reached 97.80%, and that of ResNet50 reached 97.94%. Similarly, Ong and Hamid (2022) have shown that the InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family. The InceptionV3 base model can be expanded to improve the recognition of common insect species like butterflies, dragonflies, grasshoppers, ladybirds, and mosquitoes (see https://www.kaggle.com/code/aryashah2k/insect-type-classification-inceptionv3).…”

Section: Ai Methods For Species Identificationmentioning

confidence: 97%

“…In some cases, researchers have used machine learning algorithms to analyse images of insects and accurately identify species with greater accuracy than human experts (Júnior & Rieder, 2020; Høye et al., 2021; Xia et al., 2018). In summary, the performance of AI has significant implications for the field of entomology and can improve our understanding of insect diversity (Gerovichev et al., 2021) and species distribution (Ong & Hamid, 2022).…”

Section: Ai Methods For Species Identificationmentioning

confidence: 99%

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Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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The application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

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Section: Ai Methods For Species Identificationmentioning

confidence: 97%

Section: Ai Methods For Species Identificationmentioning

confidence: 99%

Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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Hierarchical classification of insects with multitask learning and anomaly detection

Bjerge,

Geissmann,

Alison

et al. 2023

Ecological Informatics

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Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

Bjerge¹,

Geissmann

Alison

et al. 2023

Preprint

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Cameras and computer vision are revolutionising the study of insects, creating new research opportunities within agriculture, epidemiology, evolution, ecology and monitoring of biodiversity. However, a major challenge is the diversity of insects and close resemblances of many species combined with computer vision are often not sufficient to classify large numbers of insect species, which sometimes cannot be identified at the species level. Here, we present an algorithm to hierarchically classify insects from images, leveraging a simple taxonomy to (1) classify specimens across multiple taxonomic ranks simultaneously, and (2) highlight the lowest rank at which a reliable classification can be reached. Specifically, we propose multitask learning, a loss function incorporating class dependency at each taxonomic rank, and anomaly detection based on outlier analysis for quantification of uncertainty. First, we compile a dataset of 41,731 images of insects, combining images from time-lapse monitoring of floral scenes with images from the Global Biodiversity Information Facility (GBIF). Second, we adapt state-of-the-art convolutional neural networks, ResNet and EfficientNet, for the hierarchical classification of insects belonging to three orders, five families and nine species. Third, we assess model generalization for 11 species unseen by the trained models. Here, anomaly detection is used to predict the higher rank of the species not present in the training set. We found that incorporating a simple taxonomy into our model increased accuracy at higher taxonomic ranks. As expected, our algorithm correctly classified new insect species at higher taxonomic ranks, while classification was uncertain at lower taxonomic ranks. Anomaly detection can effectively flag novel taxa that are visually distinct from species in the training data. However, five novel taxa were consistently mistaken for visually similar species in the training data. Above all, we have demonstrated a practical approach to hierarchical classification based on species taxonomy and uncertainty during automated in situ monitoring of live insects. Our method is simple and versatile and could be implemented to classify a wide range of insects as well as other organisms.

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Next generation insect taxonomic classification by comparing different deep learning algorithms

Cited by 4 publications

References 21 publications

Artificial neuronal networks are revolutionizing entomological research

Artificial neuronal networks are revolutionizing entomological research

Hierarchical classification of insects with multitask learning and anomaly detection

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

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