Problem-Why we made a butterfly tracking app?
We designed this app to bring people closer to nature and make butterfly observation more engaging for everyone, while also supporting educational and academic institutions in tracking butterflies more effectively.
Our app contains AI-powered recognition, enabling anyone—from students and enthusiasts to scientists—to identify butterflies in real time, record observations, and contribute valuable data for conservation.
Target Group
-
Students and educators interested in nature and biology
-
Butterfly enthusiasts and amateur naturalists
-
Researchers and conservationists looking for organized observation data
Design Concept
Business Analytics
SWOT Analysis
Our SWOT analysis revealed that our strengths lie in interactive learning and a complete butterfly database. Our weaknesses are technical barriers and a long education and promotion cycle. Our opportunities are that ESG, CSR, and SDGs are the current social trends, but our threats are that the market for butterfly apps is limited.
STP analysis
We have used the market segmentation model. One of the core users is aged 40-65, living in Hamburg, Germany, is a teacher by profession with a passion for taking students on nature observation trips. Therefore, our market positioning approaches that of a Public Welfare Cooperation, providing Ecological Apps for educational purposes and and a comprehensive database about butterfly sights.
The Butterfly Identification App business model canvas emphasizes education and environmental conservation. Its value proposition is to provide an innovative tool that combines learning with entertainment, using AI to identify butterfly species and offering an instant, rich database. Customer segments focus on teachers, students, and environmental volunteers, making the app useful for education, learning, and species monitoring.
Key partners include schools, which serve as channels for educational promotion, and ecological research institutes, which provide scientific credibility and data support. Key activities center on developing and maintaining the AI recognition system, supported by a technical team and researchers. Customer relationships begin with free downloads, while channels include educational platforms and curriculum partnerships.
The cost structure involves app development, server maintenance, AI model updates, and researcher expenses. For revenue streams, the app currently relies on research project funding but has strong potential in school licensing, CSR collaborations, and premium subscription services.
Overall, the model balances educational value with sustainability, fostering collaboration with academia, NGOs, and corporations. It aligns with ESG and CSR initiatives, ensuring both financial viability and long-term social impact in biodiversity protection.
The 4P Marketing Matrix highlights the app’s strategy. Product: AI real-time butterfly recognition, with counting and tracking features to support education and conservation. Price: free to download, with funding support from research projects. Place: distributed through education bureaus, learning platforms, school partnerships, and major app stores (App Store, Google Play). Promotion: marketing efforts include social media campaigns (Facebook, Instagram, TikTok), teacher workshops, and campus ecotourism activities.
This mix combines technology, education, and environmental values, ensuring both accessibility and long-term sustainability.
Solution
We designed a butterfly tracking app that combines AI recognition with a database, allowing real-time identification, observation recording, and data sharing to support conservation research.
Our app combines AI-powered butterfly recognition with a user-friendly interface, allowing users to:
-
Instantly identify butterfly species
-
Record sightings with location and time
-
Track personal observations and share them with the community
-
Contribute to butterfly conservation researchz
Prototype
-
A simple interface for snapping photos of butterflies
-
Automatic species identification and information display
- Option to add notes and share findings
Future Expectations
Our current prototype uses Image Recognition in order to pre-filter pictures. However, Object Recognition has the potential to massively improve the user experience of our app, while also allowing for more innovative features being implemented. The following pictures illustrate what features we would focus on next. 
Team
-
A multidisciplinary team of students and developers passionate about nature, AI, and education
-
Roles include app development, AI model training, design thinkers and a SCRUM-master
XiaoKuo 、 
Joshua 、 Jason 、 Howard 、 Maurice 、 Patrik