Plant Disease Detection App

Plant Disease Detection using ML model and Android App

AI & Machine LearningJavaApache-2.0

Abstract

Plant Disease Detection App is an open-source AI & Machine Learning project. Plant Disease Detection using ML model and Android App. Plant Disease Detection using ML model and Android App . CropTec_Ver1.0 is an Android Application which is used for detecting crop diseases using images of crop plants . It is built using Java. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Plant Disease Detection using ML model and Android App . CropTec_Ver1.0 is an Android Application which is used for detecting crop diseases using images of crop plants .

Hindi Language is given is an option, since this application will be mostly used by villagers and English language should not be a barrier for them to access this app.

Where we get the Plant, Disease, Cause and the Accuracy (Probability) with which the model has predicted that particular Disease .

2. Objective

Plant Disease Detection using ML model and Android App

This project demonstrates how Java can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Java

5. System Requirements

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

  • JDK 11 or later
  • Maven / Gradle
  • IntelliJ IDEA, Eclipse or Android Studio
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/vermasrijan/PlantDiseaseDetectionApp.git
cd PlantDiseaseDetectionApp

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 vermasrijan and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship