Plant Disease Classification using Convolutional Neural Networks (CNN)

📌 Overview

This project implements a deep learning–based image classification system to detect and classify plant diseases from leaf images using Convolutional Neural Networks (CNNs).
The goal is to enable early and automated detection of plant diseases, which can help reduce crop losses and support precision agriculture.

The model learns discriminative visual features directly from raw image data, eliminating the need for manual feature extraction.

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Dataset

• Kaggle dataset link : https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset

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Methodology

1. Data Preprocessing

• Loaded image data using directory-based generators
• Applied resizing and normalization
• Prepared training and validation pipelines

2. Model Architecture

• Convolutional Neural Network (CNN) consisting of:
  • Convolutional layers for feature extraction
  • Pooling layers for spatial reduction
  • Fully connected (dense) layers for classification
• Activation functions such as ReLU and Softmax used

3. Training

• Model trained on labeled plant leaf images
• Loss function: Categorical Cross-Entropy
• Optimizer: Adam
• Performance monitored using accuracy and loss metrics

4. Evaluation

• Evaluated using validation accuracy and loss
• Training history visualized to analyze convergence and overfitting

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📊 Results

• The CNN successfully learned visual patterns associated with different plant diseases
• Achieved reliable classification performance on validation data
• Accuracy and loss curves indicate effective learning behavior

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🛠️ Technologies Used

• Python
• TensorFlow / Keras
• NumPy
• Matplotlib
• Jupyter Notebook

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