A deep learning tool that identifies 38 types of plant diseases from leaf photographs using a fine-tuned VGG16 CNN trained on the PlantVillage dataset of 87,000 images. Farmers upload a leaf photo and receive instant disease diagnosis with treatment recommendations.
The report, PPT slides, and source code are fully editable. Change the title page,
add your college name, modify sections, rename variables, extend features — anything your college or
guide requires. No restrictions whatsoever. The pre-trained model
is bundled inside — no training from scratch required.
👀 See What's Inside
This is a live preview of the deliverables you will receive when purchasing any project on our platform.
Academic Project Report
Professionally formatted in IEEE/University standard format. Approx 40 pages.
Presentation Slides
Clean, modern, and ready for your final year presentation. Approx 12 to 15 slides.
Internship Certificate
Official 4/8/12 week internship completion certificate.
Project Certificate
Unique QR-coded project completion certificate.
Viva Questions Prep
Detailed technical Viva Q&As with explanations.
Architecture Diagram
System workflow flowchart & data processing pipeline specs.
LinkedIn Post Draft
Ready-to-post LinkedIn content to showcase your project.
Copy-ready resume bullet points are included with this project.
• Engineered and deployed the Plant Disease Detection System, providing a robust solution for machine learning / ai challenges.
• Utilized a modern technology stack including Python, TensorFlow, CNN, VGG16, Transfer Learning, Flask, PlantVillage, Agriculture to build a highly scalable, secure, and modular application architecture.
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Resume
Points Locked
4 professionally written resume bullet points are included. Purchase
this project to unlock them.
