Overview:

The Plant Disease Prediction System is a Machine Learning and Computer Vision-based application that helps identify plant diseases by analyzing images of leaves.
Using techniques like image classification and deep learning (CNN models), the system detects whether a plant is healthy or affected by a particular disease and provides the name, cause, and possible remedies.

This project aims to assist farmers, researchers, and agricultural experts in detecting diseases early, preventing crop loss, and improving yield quality through AI-driven diagnosis.

Objectives:

• To automate the process of detecting and classifying plant diseases from leaf images.

• To use Machine Learning and Deep Learning models for accurate disease prediction.

• To provide disease details and treatment suggestions to users in real time.

• To demonstrate how AI can enhance precision agriculture and reduce manual efforts.

Key Features:

1. Leaf Image Detection: Upload a photo of a plant leaf to detect if it’s healthy or diseased.

2. AI-Powered Disease Classification: Identifies diseases like Leaf Spot, Rust, Blight, Mildew, etc. using CNN models.

3. Real-Time Detection (optional): Supports live camera scanning for instant results.

4. Remedy Suggestions: Displays information about the detected disease and its preventive measures.

5. Dataset Training: Uses large labeled image datasets for training the AI model.

6. Accuracy Metrics: Displays model performance (precision, recall, accuracy).

7. Search by Crop Type: Allows users to select crop category (tomato, rice, maize, etc.) before analysis.

8. Web or Mobile Interface: Simple and responsive UI for farmers and agricultural officers.

9. Admin Dashboard: Manage dataset, monitor predictions, and update remedy information.

10. Offline Mode (optional): Can work on devices without internet by using TensorFlow Lite.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Python (Flask/Django) / Node.js / PHP

• Machine Learning / Deep Learning:

  • Frameworks: TensorFlow, Keras, PyTorch, OpenCV, NumPy, scikit-learn

  • Model: Convolutional Neural Network (CNN) for image classification

  • Algorithms: Transfer Learning (MobileNet, VGG16, ResNet50)

• Database: MySQL / MongoDB

• Dataset:

  • Public datasets like PlantVillage or Kaggle Plant Disease Dataset

Workflow:

1. Data Collection:

   • Collect a large dataset of healthy and diseased plant leaf images.

2. Data Preprocessing:

   • Resize, normalize, and augment images for better model generalization.

3. Model Training:

   • Train a CNN model on preprocessed data to classify diseases.

4. Prediction Phase:

   • User uploads a leaf image → Model analyzes it → Predicts disease type and probability.

5. Result & Recommendation:

   • Displays diagnosis report and remedies for treatment or prevention.