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Artificial Intelligence · Major Project
Medicinal Plant Identification
Identifies Indian medicinal plants from a leaf photo and lists their uses
3-4 weeks build
Overview
Leaf photos of common Indian medicinal plants such as tulsi, neem and curry leaf are classified by a fine-tuned EfficientNet trained on a public Kaggle leaf dataset. Each prediction links to a curated MongoDB entry with the plant's common and botanical names and traditional uses, presented as reference information rather than medical advice.
What makes it stand up
- 01Leaf classifier covering dozens of Indian medicinal species
- 02Background removal step so cluttered photos still classify
- 03Plant knowledge base with common, regional and botanical names
- 04Uses shown as traditional reference, with a clear not-medical-advice note
- 05Top-three suggestions when the model is unsure
Module breakdown
- 01
Leaf preprocessing
Segmentation of the leaf from its background with OpenCV.
- 02
Classifier
Fine-tuned EfficientNet with class-weighted loss.
- 03
Knowledge base
MongoDB collection of species, names and documented uses.
- 04
Web app
Flask upload page that returns the species card.
- 05
Evaluation
Per-species F1 and a confusion matrix of look-alike leaves.
After this, you will be able to
- Explain how the model separates visually similar leaves
- Defend the decision to show top-three results under low confidence
- Demo identification on leaves collected from campus
Technology stack
- Python
- TensorFlow
- OpenCV
- Flask
- MongoDB
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support