BeeMachine.ai: Empowering Bee Conservation Through AI-Powered Identification

BeeMachine.ai is a powerful AI-driven tool developed by the Spiesman Lab at Kansas State University, in collaboration with researchers from the University of Wisconsin–Madison and Kansas State University, that uses advanced computer vision to identify bee species and other flower-visiting insects with remarkable precision. Designed for both citizen scientists and conservationists, it enables users to upload photos of bees from multiple angles, receive accurate species or genus-level identifications, and contribute to a global open database dedicated to wild bee conservation. With a model trained on over 1.2 million images from reputable sources like GBIF and community science initiatives, BeeMachine.ai achieves 93.7% accuracy in species identification and 99.4% in top-3 predictions. Its cross-platform accessibility via web and mobile app makes it easy to use anywhere, while its commitment to privacy ensures that user data is anonymized and never sold. By combining cutting-edge AI with real-world ecological research, BeeMachine.ai turns everyday observations into valuable scientific contributions.

Key Features:

• AI-Powered Insect Identification: Accurately identifies 221 bee species and 128 bee genera, plus other pollinators like wasps, flies, hoverflies, beetles, and butterflies/moths.
• Multi-Angle Image Upload: Supports uploading multiple photos of the same bee from different angles to improve identification accuracy.
• Interactive Bee Tracking Map: Visualize and track bee sightings geographically in real time.
• Community Science Contribution: User-submitted sightings and images help build a public, open database for conservation research.
• Cross-Device Access: Available on both web and mobile platforms for seamless use across devices.
• High Accuracy Model: Powered by a modified EfficientNetV2 neural network trained on over 1.2 million images, delivering 93.7% species-level accuracy and 99.4% top-3 accuracy.
• Genus-Level Predictions: Provides genus-level identification when species-level certainty is low.
• Public Domain Image Sharing: Submitted photos are released under CC0 license, contributing to open biodiversity science.
• Privacy-First Design: User data is anonymized when shared; personally identifiable information is never sold.
• User Data Control: Allows users to delete their account and individual sightings at any time, with no recovery option.

Pricing: BeeMachine.ai is currently offered as a free tool with no cost to users, supporting open access and community-driven science.

Conclusion: BeeMachine.ai is an innovative, accessible, and impactful AI tool that bridges citizen science and conservation biology, empowering users worldwide to help protect pollinators through smart, accurate, and privacy-conscious insect identification.