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Optimized CNN-based ensemble deep learning approach for potato leaf disease detection with data augmentation.

Achin Jain1, Arun Kumar Dubey1, Sunil K Singh2

  • 1Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Scientific Reports
|May 18, 2026
PubMed

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Summary
This summary is machine-generated.

Optimized convolutional neural networks (CNNs) effectively classify potato leaf diseases. Ensemble Deep Learning (EDL10) achieved 97.0% accuracy, demonstrating the power of data balancing and ensemble methods for disease detection.

Area of Science:

  • Computer Science
  • Agricultural Science
  • Plant Pathology

Background:

  • Potato diseases like early and late blight significantly impact crop yield.
  • Accurate and timely disease detection is crucial for effective management strategies.
  • Traditional methods for disease identification can be labor-intensive and subjective.

Purpose of the Study:

  • To develop and evaluate optimized Convolutional Neural Networks (CNNs) for classifying potato leaf diseases.
  • To compare the performance of different optimizers (ADAM, SGD, RMSPROP, ADAMAX) within CNN models.
  • To investigate the effectiveness of Ensemble Deep Learning (EDL) and data augmentation for improving disease classification accuracy.

Main Methods:

  • Utilized a dataset of healthy, early blight, and late blight potato leaf images from Kaggle's Plant Village.

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  • Preprocessed images by resizing to 128x128 pixels and splitting into 80% training and 20% testing sets.
  • Designed CNN models with convolutional, pooling, and fully connected layers, trained using sparse categorical cross-entropy loss and early stopping. Implemented data augmentation and an ensemble of four CNN models (EDL10).

    • Main Results:

    • The EDL10 model, an ensemble of four CNNs with different optimizers, achieved the highest accuracy of 97.0%.
    • Data augmentation was crucial for balancing the dataset, particularly by increasing the number of healthy leaf images.
    • Various performance metrics including accuracy, loss curves, confusion matrix, ROC curve, precision-recall curve, classification report, and F1 score were used for evaluation.

    Conclusions:

    • Optimized CNNs, especially when combined in an ensemble approach (EDL10), are highly effective for potato leaf disease classification.
    • Data balancing through augmentation significantly enhances model performance.
    • The study underscores the potential of deep learning for automated and accurate plant disease detection in agriculture.