Plant Disease Detection Keras

Abstract

1. Introduction

In this project, a neural network model was built using Tensorflow. The model detects if a plant is suffering from a disease(Rust or Powdery Mildew). The model was then deployed as an API using the FastAPI framework.

2. Objective

Plant Disease Detection model built with Keras and FastAPI

This project demonstrates how Jupyter Notebook, Docker, FastAPI can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Nneji123/Plant-Disease-Detection-Keras.git
cd Plant-Disease-Detection-Keras

1. Clone the repository:
2. Change the directory:
3. Login to Heroku
4. Create your application
5. Build the image and push to Container Registry:
6. Then release the image to your app:

git clone https://github.com/Nneji123/Plant-Disease-Detection-Keras.git

cd Plant-Disease-Detection-Keras

heroku login
heroku container:login

heroku create your-app-name

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Nneji123 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.