DenseNet169_Mendeley

DenseNet169 Potato Leaf Disease Classifier (Mendeley Dataset)

Model Description

This model is a fine-tuned DenseNet169 CNN trained for potato leaf disease classification on the Mendeley Potato Disease Dataset. The model is designed to detect and classify 7 different potato plant disease categories using leaf images.

Key Features

• Architecture: DenseNet169 (pre-trained on ImageNet, fine-tuned on Mendeley dataset)
• Framework: TensorFlow/Keras
• Input: RGB images (224×224 pixels)
• Classes: 7 disease categories
• Format: .keras (Keras 3 format)

Model Performance

Overall Metrics

• Accuracy: 91.56%
• Precision: 91.98%
• Recall: 91.58%
• F1-Score: 91.62%
• Matthews Correlation Coefficient (MCC): 0.8976
• Balanced Accuracy: 90.47%

Per-Class Performance

Test Set Size: 616 images

Dataset Information

Mendeley Potato Disease Dataset

• Total Images: ~3,076 images
• Classes: 7 disease categories
  • Bacteria
  • Fungi
  • Healthy
  • Nematode
  • Pest
  • Phytophthora
  • Virus
• Data Source: Mendeley Data - Potato Leaf Diseases (ptz377bwb8)
• Characteristics: Real-world field images with varying lighting, angles, and natural conditions

Usage

Installation

pip install tensorflow keras

Load and Use the Model

import tensorflow as tf
from tensorflow import keras
from PIL import Image
import numpy as np

# Load the model
model = keras.models.load_model('best.keras')

# Prepare image
image = Image.open('path/to/potato_leaf.jpg')
image = image.resize((224, 224))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)

# Make prediction
predictions = model.predict(image_array)
class_names = ['Bacteria', 'Fungi', 'Healthy', 'Nematode', 'Pest', 'Phytophthora', 'Virus']
predicted_class = class_names[np.argmax(predictions)]
confidence = np.max(predictions)

print(f"Predicted Disease: {predicted_class}")
print(f"Confidence: {confidence:.2%}")

Using Hugging Face Transformers

# Coming soon - optimized inference pipeline

Training Details

Architecture

• Base Model: DenseNet169 (ImageNet pre-trained weights)
• Input Size: 224×224×3
• Total Parameters: ~14M
• Custom Head: Fine-tuned on Mendeley dataset

Data Preprocessing

• Image resizing: 224×224
• Normalization: ImageNet normalization
• Augmentation: Random rotations, flips, brightness adjustments

Training Configuration

• Optimizer: Adam
• Loss Function: Categorical Cross-Entropy
• Metrics: Accuracy, Precision, Recall, F1-Score
• Early Stopping: Enabled to prevent overfitting

Model Card Information

• Model Name: DenseNet169 Potato Leaf Disease Classifier
• Created: 2026
• Framework: Keras/TensorFlow
• Model Type: Image Classification (CNN)
• License: Apache 2.0

Intended Use

This model is intended for:

• Primary Use: Classification of potato leaf diseases in agricultural applications
• Supported Use Cases:
  • Automated disease detection systems
  • Agricultural advisory systems
  • Precision farming applications
  • Research and baseline comparisons

Limitations

• Dataset Scope: Trained exclusively on Mendeley dataset; performance may vary on other potato disease datasets
• Class Imbalance: Some classes (Nematode: 14 samples) have limited training data
• Environmental Factors: Model trained on field conditions; controlled environment images may perform differently
• Image Quality: Requires clear leaf images; heavily obscured or blurry images may reduce accuracy
• Biological Variation: Limited to potato plant diseases; not suitable for other crop diseases

Related Models

• DenseNet201 Mendeley: Alternative architecture with similar performance
• ResNet50 Mendeley: Baseline comparison model
• Models on PlantVillage Dataset: 3-class variant (Early Blight, Late Blight, Healthy)

Citation

If you use this model, please cite:

@dataset{mendeley_potato_disease,
  title = {Potato Leaf Diseases Dataset},
  url = {https://data.mendeley.com/datasets/ptz377bwb8/1}
}

Contact & Support

For questions or issues regarding this model:

• Repository: Potato-leaf-disease-prediction
• Dataset Issues: Reference the Mendeley dataset documentation

Disclaimer

This model should be used as a reference tool and not as the sole basis for agricultural decisions. Always consult with agricultural experts for accurate disease diagnosis and treatment recommendations.

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Model Version: 1.0
Last Updated: May 2026
Framework: TensorFlow 2.x / Keras 3
Python Version: 3.8+