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AI & Machine Learning Project

DeepGuard: CNN-Based Plant Disease Classifier

Python TensorFlow OpenCV Flask

Project Overview

A deep learning convolutional neural network (CNN) system designed to detect and classify crop leaf diseases from uploaded images with statistical confidence readouts.

Deliverables & Support Checklist

  • Complete Source Code: Clean, well-commented code, database schemas, and setup configurations (Github/Zip format).
  • Remote Setup Support: Complete environment installation and compiler/server startup runs done via AnyDesk or TeamViewer by a senior developer.
  • Project Report & PPT template: Synced system flowcharts, UML diagrams, hardware/software specs, and presentation slides.
  • Viva Voce Assistance: 1-on-1 code walkthrough explanation calls to prepare you for department evaluations and external examiners.
  • Post-Submission Modifications: Lifetime bug-fixing support and code tweaks requested by project coordinators.

Viva-Voce Q&A Highlights

Q1: What is the primary architecture of this system?

A: It is structured as a standalone full-stack microservices simulation. It splits rendering blocks (HTML, modular JavaScript components) from backend logic handling algorithms, keeping standard RESTful conventions.

Q2: How is security handled inside this model?

A: Code modules execute standard local authentication. Security structures utilize cryptographic hashing (such as SHA-256 block hash references) and local password salts where required.

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