AI
Image Classifier for Plants
CNN model for leaf disease classes.
Project overview
Image Classifier for Plants is a mentor-guided AI project designed for university submission. Computer vision classifier to detect plant leaf diseases from images with confidence scores and a web demo. The build uses Python, TensorFlow, Keras, Flask and is structured so you can explain architecture, modules, and test cases during viva. Typical delivery timeline is 4–6 weeks depending on package and college deadlines.
Best suited for: CSE / AI&DS students focusing on computer vision or NLP applications
Tech stack
Objectives
- Design and implement a working solution for “Image Classifier for Plants” aligned to academic evaluation criteria.
- Demonstrate core features such as Image upload, Class labels, Confidence.
- Produce documentation (synopsis, report chapters, and PPT) suitable for guide approval.
- Prepare viva-ready explanations for design choices, tech stack, and future enhancements.
Key modules
- Module: Image upload
- Module: Class labels
- Module: Confidence
- Documentation, deployment notes, and viva Q&A
Key features
- Image upload
- Class labels
- Confidence
- Sample dataset
Deliverables
- Notebook
- Model
- Report
- PPT
Learning outcomes
- Runnable source code with setup guide
- University-style report draft
- Presentation deck (PPT)
- Mentor review checkpoints
