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Updated: Jun 25, 2026

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Deep Learning for Disease Detection: Building a Leaf Image Classifier for Roses.

Mihnea Ș Georgescu1, Silviu Răileanu1, Camelia Ungureanu2

  • 1Faculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independentei, Sector 6, 060042 Bucharest, Romania.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study shows convolutional neural networks (CNNs) can accurately detect rose diseases from leaf images. This machine learning approach aids automated plant monitoring and timely disease intervention.

Area of Science:

  • Agricultural Science
  • Computer Science

Background:

  • Automated plant monitoring requires early and reliable detection of rose diseases.
  • Leaf image analysis with machine learning presents a viable solution for disease identification.

Purpose of the Study:

  • To evaluate the effectiveness of convolutional neural networks (CNNs) for binary classification of diseased rose leaves.
  • To compare different CNN architectures and image preprocessing techniques for disease detection.

Main Methods:

  • Three CNN architectures were tested: a baseline CNN trained from scratch and two residual networks fine-tuned using transfer learning.
  • Two preprocessing strategies were compared: hue-based leaf isolation and grayscale conversion.
  • The models were evaluated on a held-out test set.
Keywords:
agriculturedeep learningimage classificationleaf disease detectionmachine visionneural networks

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Main Results:

  • All evaluated CNN models demonstrated strong classification performance in distinguishing diseased rose leaves.
  • The study identified potential limitations in generalization across diverse datasets and conditions.

Conclusions:

  • CNN-based image analysis is a feasible method for detecting rose diseases.
  • The findings support the integration of this technology into automated plant monitoring systems.