---

🌿 Aloe Vera Disease Classification Project

This project focuses on detecting and classifying diseases in aloe vera plants using a Convolutional Neural Network (CNN). The system identifies three conditions—Healthy, Rot, and Rust—to support early detection, promote sustainable farming practices, and aid farmers in maintaining healthy crops.

---

🚀 Project Overview

Objectives:

• Develop an automated system to detect and classify aloe vera diseases.
• Provide early disease detection to improve agricultural outcomes.
• Create a user-friendly web application for real-time classification.

Key Features:

• Disease Detection: Identifies Healthy, Rot, and Rust conditions.
• Augmentation Support: Enhances model robustness with augmented data.
• Background Removal: Improves model accuracy by focusing on essential features.
• Web Application: Deployable Flask-based web app for real-time disease classification.

---

📊 Dataset

Structure:

The dataset contains 9000 images categorized into three classes:

Classes:

1. Healthy
2. Rot
3. Rust

---

🛠️ Technology Stack

Hardware:

• Processor: AMD Ryzen 5 Hexa Core 5600H
• RAM: 8 GB
• Storage: 512 GB SSD
• Graphics: NVIDIA GeForce RTX 3050 Ti (4 GB)

Software:

• AI/ML Frameworks: TensorFlow, Keras
• Front-end: HTML, CSS, JavaScript
• Back-end: Flask
• Libraries: Scikit-learn, Pandas, NumPy, Matplotlib
• Deployment: Flask Web Application

---

⚙️ How to Run the Project

1. Clone the repository:

   git clone https://github.com/Sabale-37/Aloevera-Disease-Classification-Using-CNN
   cd Aloevera-Disease-Classification-Using-CNN

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask app:

   python main.py

4. Open the web interface:

   • Navigate to http://localhost:5000 in your web browser.

---

📈 Model Training

Steps:

1. Data Preprocessing:

   • Normalize images.
   • Data augmentation to enhance model performance.

2. Model Architecture:

   • Implemented a Convolutional Neural Network (CNN) with Keras.
   • Configured for classification with three output classes.

3. Training:

   • Used cross-entropy loss and the Adam optimizer.
   • Evaluated using accuracy, precision, and recall metrics.

---

🌟 Future Enhancements

• Integrate real-time detection using a mobile app.
• Enhance model accuracy with more diverse datasets.
• Implement a feedback mechanism for continuous model improvement.

---

👥 Contributors

---