About
About this system and how to use it safely
An intelligent system for medicinal plant identification and symptom-based herbal treatment recommendation, built as a final-year Computer Science major project.
What this system does — and does not — do
It does
- Identify a plant from an uploaded photo through a model-ready pipeline
- Match everyday symptom descriptions to recorded traditional uses
- Explain every score with the keywords that produced it
- Present botany, properties, remedies and precautions together
- Save analyses, collect feedback and produce a printable report
It does not
- Diagnose any disease or condition
- Prescribe treatment, dosage or duration
- Replace a doctor, pharmacist or registered practitioner
- Guarantee that a plant identified from a photo is safe to consume
- Detect herb–drug interactions specific to your medication
Technology
Front end
React 19, TypeScript, Tailwind CSS and accessible shadcn-based components.
API & ML
FastAPI serving a MobileNetV2 transfer-learning classifier (224×224 input) and a TF-IDF + cosine similarity recommender.
Data & storage
Curated plant, symptom and remedy datasets served by the API, with history and feedback in the cloud database.
Known limitations of this build
- Results are produced only by the analysis service. If the trained model or the fitted symptom matcher is not installed, the affected page says so instead of showing a result.
- No accuracy figure is claimed anywhere in the interface; the numbers shown are the model's own confidence and similarity values for your input.
- Grad-CAM heatmaps are computed from the trained model itself, and nothing is drawn when the model does not return one.
- The knowledge base only contains what the curated plant dataset contains, so many symptoms will legitimately return no match.
- History and feedback are stored in your account, so they are only visible when you are signed in.