PlantPal
Identify a plant from a photo, diagnose what ails it, and get care advice — conversationally.
Problem
Working out what a plant is — and then what is wrong with it — usually means guesswork or scattered forum threads. People want a clear answer: what is this, is it healthy, and what should I do about it, without having to be a botanist.
Solution
PlantPal recognises a plant species from a photo and, in the same flow, diagnoses disease and recommends care. A vision model handles recognition; a reasoning LLM turns the raw classification into botanical detail, care instructions and treatment advice, explained in plain language. It keeps conversation memory, so follow-up questions stay grounded in the same plant and image.
Features
- Plant species recognition from an uploaded image
- Disease detection and diagnosis
- Care and watering recommendations
- Treatment and prevention advice
- Conversational follow-up with conversation memory
- Plant encyclopedia
Architecture
- Frontend
- Backend API
- Vision AI model
- Reasoning LLM
- Plant knowledge base
- Response generation
Tech
- Angular
- Spring Boot
- PostgreSQL
- Docker
- Vision Models
- LLMs
- REST APIs
- Authentication
Challenges
- Combining two very different AI models — a vision classifier and a reasoning LLM — into one coherent answer.
- Keeping the conversation anchored to the actual image and classification rather than letting the LLM drift.
Lessons
- Prompt engineering matters as much as model choice: the product only feels intelligent when the reasoning step is tied to real classifier output and conversation memory.
Future work
- Broaden the species database
- On-device / offline recognition
- Richer care scheduling and reminders