Group Kilo - From AI to BI

Why It Matters

Bee health underpins pollination, food security, and ecosystem resilience; a digital inspection tool can dramatically reduce disease spread and support sustainable agriculture. By empowering beekeepers with AI diagnostics, the app accelerates early intervention and data‑driven research.

Key Takeaways

• AI identifies bee diseases from photos
• Mobile app crowdsources hive health data
• Provides real‑time guidance for beekeepers
• Bridges gap caused by inspector shortage
• Supports research and environmental sustainability

Pulse Analysis

Honeybee populations worldwide face mounting pressures from pathogens, climate change, and habitat loss, and the Cambridge area is no exception. Local beekeepers contribute to biodiversity and campus life, yet they lack sufficient on‑ground inspection resources. Introducing a smartphone‑based platform transforms every beekeeper into a data point, turning routine hive checks into a coordinated surveillance network that can detect outbreaks before they devastate colonies.

The core of the application is a convolutional neural network trained on thousands of annotated bee images, enabling rapid disease identification from a simple photo. Users receive instant diagnostic feedback, recommended treatment steps, and links to best‑practice guides. Integrated data capture logs hive conditions, treatment outcomes, and geographic trends, feeding a centralized research database. This crowdsourced intelligence not only empowers individual beekeepers but also provides researchers with granular, real‑time insights previously unavailable through sporadic field inspections.

Beyond immediate disease management, the platform creates a sustainable ecosystem for continuous learning and commercial opportunity. Aggregated data can inform regional policy, support grant applications, and attract agritech investors seeking scalable environmental solutions. As pollinator health becomes a strategic priority for food supply chains, the app positions Cambridge as a model for tech‑enabled apiculture, illustrating how AI and mobile connectivity can bridge expertise gaps and drive measurable ecological and economic benefits.

Original Description

Client - Philip Turon, Cambridgeshire Beekeepers Assocation

Ethan Smith, Leo Vadgama, Matt Occleshaw, Oliver Gibson, Soham Shah, Sophia Chan

Beekeeping is valuable for the environment, an enjoyable activity in many Cambridge colleges, and makes delicious honey. But honeybees in the Cambridge area are at threat from diseases that could be better detected and controlled, if only there were more bee inspectors (BI's)! Your task is to create a mobile application that supports this community, including use of AI to recognise some serious diseases from photos, collects data for bee research, and guides beekeepers to make better use of the tools and support available.