Plant Disease Detection And Solution

Abstract

1. Introduction

This project is based on Plant Disease Detection using Image Classification with Solution for detected disease of plant. This project comprises of Machine Learning part and Android Application Development part. On Machine Learning side, we developed a well-trained model for image classification which will help to classify the disease of the infected plant. On Android App Dev. part, we click images of infected plant and compare with our classification model, then it detects disease of infected plant and also provides solutions for disease of infected plant.

Project on Plant Disease detection and Solutions using Image Classification technique.

[![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url]

2. Objective

Plant disease detection and Solution using Image Classification

This project demonstrates how Jupyter Notebook, TensorFlow, Machine Learning 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
• Android Studio (latest)
• JDK 17
• Android device or emulator
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/DevilStudio27/Plant-Disease-Detection-and-Solution.git
cd Plant-Disease-Detection-and-Solution

1. Download and Install Python from above link if not installed.
2. Download Anaconda from above link if not installed.
3. For Installing Anaconda, Follow steps performed from below given Video Link.
4. [Setup Anaconda][setup-anaconda]
5. After Installing, open Anaconda and launch Jupyter Notebook.
6. Now open notebook files from project folder.

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 DevilStudio27 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.