Deep learning-based hierarchical insect classification using camera trap imagery

• URL: http://arxiv.org/abs/2607.28005v1
• Date: Thu, 30 Jul 2026 10:51:38 GMT
• Title: Deep learning-based hierarchical insect classification using camera trap imagery
• Abstract summary: Declining insect populations make reliable biodiversity monitoring increasingly urgent.<n> monitoring of insect biodiversity is hampered by a lack of standardised data.<n>Deep learning-based image classifiers have the potential to transform and scale insect biodiversity monitoring.
• Score: 1.2282296429222195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Declining insect populations make reliable biodiversity monitoring increasingly urgent, yet monitoring of insect biodiversity is hampered by a lack of standardised data and by costly and time-consuming manual identification by expert entomologists. Deep learning-based image classifiers, processing data from automated non-lethal camera traps, have the potential to transform and scale insect biodiversity monitoring. However, challenges remain in acquiring expert-annotated datasets, developing model architectures that generalise well across diverse taxonomic levels and training models on highly imbalanced data. Hierarchical data also benefits from designing models that default to higher-confidence, coarser-level predictions, when uncertain about finer taxonomic levels. In this paper we address these challenges with a deep learning-based hierarchical classification model. First, we present a manually curated, long-tailed dataset of around one million images of insects, extracted from 1,801 camera-trap video recordings and annotated with a five-level, 34-class hierarchy. Further, we adapt a hierarchical classification model architecture to a five-level variable-depth hierarchy, with class-balanced weighting. Our model improves on non-hierarchical classifiers by leveraging biological taxonomy to extract granularity-specific visual features and makes hierarchy-consistent predictions to the deepest taxonomic level that meets a confidence threshold (T = 0.6). Our model achieved a per-level accuracy of 80-99% on test data, across five levels of hierarchy. Furthermore ...

Related papers

• Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification [50.20892407648858]
  Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories.<n>Existing methods often produce predictions that are inconsistent across taxonomic levels.<n>We propose a group-balanced design, ensuring each taxonomic level receives adequate optimization.<n>We train our model with TreeOfLife-10M based on BioCLIP and evaluate it across multiple hierarchical classification benchmarks.
  arXiv  Detail & Related papers  (2026-06-20T02:12:05Z)
• Learning Consistent Taxonomic Classification through Hierarchical Reasoning [61.372270953201955]
  We propose a two-stage, hierarchy-based reasoning framework designed to improve leaf-level accuracy and hierarchical consistency in taxonomic classification.<n>Our framework, implemented on the Qwen2.5-VL-7B model, outperforms its original 72B counterpart by over 10% in both leaf-level and hierarchical consistency accuracy.
  arXiv  Detail & Related papers  (2026-01-21T03:00:00Z)
• Hierarchical Deep Learning for Diatom Image Classification: A Multi-Level Taxonomic Approach [0.0]
  We introduce DiatomNet (H-COFGS), a hierarchical convolutional network with five cascaded heads that jointly predict class, order, family, genus, and species.<n>Using a filtered dataset of 1,456 diatom images covering 82 species, we compare hierarchical and flat models under identical settings.<n>H-COFGS matches flat baselines at the species level (69.4% accuracy) while outperforming at all upper taxonomic levels.
  arXiv  Detail & Related papers  (2025-12-07T01:06:13Z)
• BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning [60.80381372245902]
  We find emergent behaviors in biological vision models via large-scale contrastive vision-language training.<n>We train BioCLIP 2 on TreeOfLife-200M to distinguish different species.<n>We identify emergent properties in the learned embedding space of BioCLIP 2.
  arXiv  Detail & Related papers  (2025-05-29T17:48:20Z)
• BeetleVerse: A Study on Taxonomic Classification of Ground Beetles [0.310688583550805]
  Ground beetles are a highly sensitive and speciose biological indicator, making them vital for monitoring biodiversity.<n>In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets.<n>Our results show that the Vision and Language Transformer combined with an head is the best performing model, with 97% accuracy at genus and species level.
  arXiv  Detail & Related papers  (2025-04-18T01:06:37Z)
• Low Cost Machine Vision for Insect Classification [33.7054351451505]
  We present an imaging method as part of a multisensor system developed as a low-cost, scalable, open-source system. The system is evaluated exemplarily on a dataset consisting of 16 insect species of the same as well as different genus, family and order. It was proved that image cropping of insects is necessary for classification of species with high inter-class similarity.
  arXiv  Detail & Related papers  (2024-04-26T15:43:24Z)
• Comparison between transformers and convolutional models for fine-grained classification of insects [7.107353918348911]
  We consider the taxonomical class of Insecta. The identification of insects is essential in biodiversity monitoring as they are one of the inhabitants at the base of many ecosystems. We have billions of images that need to be automatically classified and deep neural network algorithms are one of the main techniques explored for fine-grained tasks.
  arXiv  Detail & Related papers  (2023-07-20T10:00:04Z)
• A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset [18.211840156134784]
  This paper presents a curated million-image dataset, primarily to train computer-vision models capable of providing image-based taxonomic assessment. The dataset also presents compelling characteristics, the study of which would be of interest to the broader machine learning community.
  arXiv  Detail & Related papers  (2023-07-19T20:54:08Z)
• Fine-Grained ImageNet Classification in the Wild [0.0]
  Robustness tests can uncover several vulnerabilities and biases which go unnoticed during the typical model evaluation stage. In our work, we perform fine-grained classification on closely related categories, which are identified with the help of hierarchical knowledge.
  arXiv  Detail & Related papers  (2023-03-04T12:25:07Z)
• Do We Really Need a Learnable Classifier at the End of Deep Neural Network? [118.18554882199676]
  We study the potential of learning a neural network for classification with the classifier randomly as an ETF and fixed during training. Our experimental results show that our method is able to achieve similar performances on image classification for balanced datasets.
  arXiv  Detail & Related papers  (2022-03-17T04:34:28Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.