bonndata Dataverse OAI Archive
metadata only
CC BY 4.0
Images and Annotations for "Evaluating AI-Assisted Pollinator Detection in Agricultural Fields"
Pollinator monitoring is essential for understanding and addressing the decline of insect species and their populations. While computer vision techniques provide new tools for monitoring pollinators, there is a need to develop well-annotated datasets to support artificial intelligence (AI) methods f
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Horizon scanning for European wild pollinators identifies world-leading legislation as a key opportunity for pollinators#1146467
- Delivering biodiversity and pollination services on farmland: a comparison of three wildlife-friendly farming schemes#649321
- Anthropogenic modification shifts pollinator phenology in the eastern USA#784563
- Fine-Grained Recognition of Insect Pests from Digital Images: A Survey.#156876
- Navigating through the high species diversity of wild bees (Hymenoptera: Apoidea) requires more than identification keys#796441
Record · ID 1110945
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive