IntermediateSoftware trackIN-CP-03
Crop Disease Detection with Edge Deployment
Detect crop disease from leaf images, then quantise the model to run offline on a low-cost phone or Raspberry Pi.
10 Guided Steps120h Internship TierSubmit: GitHub repo or evidence pack + live demo / report + README + milestone logbookVerified CertificateAI Rubric Graded
Program Overview
Detect crop disease from leaf images, then quantise the model to run offline on a low-cost phone or Raspberry Pi.
What you submit
GitHub repo or evidence pack + live demo / report + README + milestone logbook
How it is graded
Signals: Milestone gates met; measurable result reported; documentation; deployment or evidence reachable; reflective log
- Functionality & requirements30
- Technical quality & architecture25
- Documentation15
- Deployment & evidence15
- Innovation10
- Presentation5
Pass 60% · Merit 75% · Distinction 90%
Learning Outcomes
120-hour India programme. Maps to the Smart India Hackathon agriculture theme. Offline operation matches rural reality.
Target Audience
Engineering Students
Diploma, B.Tech, B.E., BCA, MCA, or M.Tech students seeking practical experience.
Aspiring Developers
Developers who want to build proof-of-work capstone projects for tech resumes.
Job Seekers
Graduates looking for AI-evaluated code review & verified industry credentials.