BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
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Abstract (arXiv)
We introduce BioTrove, the largest publicly accessible dataset designed to advance AI applications in biodiversity. Curated from the iNaturalist platform and vetted to include only research-grade data, BioTrove contains 161.9 million images, offering unprecedented scale and diversity from three primary kingdoms: Animalia ("animals"), Fungi ("fungi"), and Plantae ("plants"), spanning approximately 366.6K species. Each image is annotated with scientific names, taxonomic hierarchies, and common names, providing rich metadata to support accurate AI model development across diverse species and ecosystems. We demonstrate the value of BioTrove by releasing a suite of CLIP models trained using a subset of 40 million captioned images, known as BioTrove-Train. This subset focuses on seven categories within the dataset that are underrepresented in standard image recognition models, selected for their critical role in biodiversity and agriculture: Aves ("birds"), Arachnida ("spiders/ticks/mites"), Insecta ("insects"), Plantae ("plants"), Fungi ("fungi"), Mollusca ("snails"), and Reptilia ("snakes/lizards"). To support rigorous assessment, we introduce several new benchmarks and report model accuracy for zero-shot learning across life stages, rare species, confounding species, and multiple taxonomic levels. We anticipate that BioTrove will spur the development of AI models capable of supporting digital tools for pest control, crop monitoring, biodiversity assessment, and environmental conservation. These advancements are crucial for ensuring food security, preserving ecosystems, and mitigating the impacts of climate change. BioTrove is publicly available, easily accessible, and ready for immediate use.
Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 12 of its 52 references that are in the map. First posted on arXiv 25 Jun 2024.
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12 of 52 references.
References · 12
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
- 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
- 2019A Study of BFLOAT16 for Deep Learning Trainingcited by 227
- 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 159
- 2018Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objectscited by 143
- 2018DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learningcited by 69
- 2020Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysiscited by 61
- 2023A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Datasetcited by 30
- 2021Danish Fungi 2020 -- Not Just Another Image Recognition Datasetcited by 27
- 2022Bugs in the Data: How ImageNet Misrepresents Biodiversitycited by 24
- 2023Exploring Dataset-Scale Indicators of Data Qualitycited by 1
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Citing papers per year: 2025: 14; 2026: 5.
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- 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 48
- 2025The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignmentcited by 5
- 2025Global and Local Entailment Learning for Natural World Imagerycited by 3
- 2025BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Modelscited by 3
- 2025Modality Alignment across Trees on Heterogeneous Hyperbolic Manifoldscited by 3
- 2025Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoringcited by 2
- 2025QuARI: Query Adaptive Retrieval Improvementcited by 2
- 2026TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Lifecited by 2
- 2025TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Modelscited by 1
- 2025ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecologycited by 1
- 2025CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selectioncited by 1
- 2025Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuringcited by 1
7 more
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Modelscited by 1
- 2025CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversitycited by 0
- 2025Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priorscited by 0
- 2026Evaluating Proactive Risk Awareness of Large Language Modelscited by 0
- 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysiscited by 0
- 2026Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classificationcited by 0
- 2026Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAGcited by 0
- 2026Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG27 Jul 2026
- 2026Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification20 Jun 2026
- 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis5 May 2026
- 2026TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life27 Mar 2026
- 2026Evaluating Proactive Risk Awareness of Large Language Models24 Feb 2026
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Models24 Dec 2025
- 2025The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment26 Nov 2025
- 2025CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selection23 Nov 2025
- 2025Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring16 Nov 2025
- 2025ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology4 Nov 2025
- 2025Modality Alignment across Trees on Heterogeneous Hyperbolic Manifolds31 Oct 2025
- 2025BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models23 Oct 2025
7 more
- 2025Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors27 Aug 2025
- 2025Global and Local Entailment Learning for Natural World Imagery26 Jun 2025
- 2025TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models29 May 2025
- 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning29 May 2025
- 2025QuARI: Query Adaptive Retrieval Improvement27 May 2025
- 2025CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity16 May 2025
- 2025Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring3 Mar 2025
- 2025Global and Local Entailment Learning for Natural World Imagerycited by 3
- 2025Modality Alignment across Trees on Heterogeneous Hyperbolic Manifoldscited by 3
- 2025QuARI: Query Adaptive Retrieval Improvementcited by 2
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Modelscited by 1
- 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysiscited by 0
- 2026CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis5 May 2026
- 2025MarineEval: Assessing the Marine Intelligence of Vision-Language Models24 Dec 2025
- 2025Modality Alignment across Trees on Heterogeneous Hyperbolic Manifolds31 Oct 2025
- 2025Global and Local Entailment Learning for Natural World Imagery26 Jun 2025
- 2025QuARI: Query Adaptive Retrieval Improvement27 May 2025
- 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 48
- 2025The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignmentcited by 5
- 2025BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Modelscited by 3
- 2025Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoringcited by 2
- 2026TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Lifecited by 2
- 2025TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Modelscited by 1
- 2025ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecologycited by 1
- 2025CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selectioncited by 1
- 2025Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuringcited by 1
- 2025CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversitycited by 0
- 2025Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priorscited by 0
- 2026Evaluating Proactive Risk Awareness of Large Language Modelscited by 0
12 most cited of 14 All 14 (JSON)
- 2026Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG27 Jul 2026
- 2026Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification20 Jun 2026
- 2026TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life27 Mar 2026
- 2026Evaluating Proactive Risk Awareness of Large Language Models24 Feb 2026
- 2025The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment26 Nov 2025
- 2025CHIPS: Efficient CLIP Adaptation via Curvature-aware Hybrid Influence-based Data Selection23 Nov 2025
- 2025Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring16 Nov 2025
- 2025ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology4 Nov 2025
- 2025BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models23 Oct 2025
- 2025Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors27 Aug 2025
- 2025TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models29 May 2025
- 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning29 May 2025
12 newest of 14 All 14 (JSON)