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Updated: Mar 21, 2026

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Simultaneous multi-disease detection from the same leaf: a generalized approach using deep learning and image

Imane Bouacida1,2, Brahim Farou3,4, Lynda Djakhdjakha3,4

  • 1LabSTIC Laboratory, 8 Mai 1945 University, Guelma, 24000, Algeria. bouacida.imane@univ-guelma.dz.

Environmental Monitoring and Assessment
|March 20, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning model for simultaneous multi-plant disease detection on single leaves. The method isolates symptoms for accurate recognition and prevalence estimation, showing promise for diverse agricultural applications.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Plant diseases pose a significant threat to global agricultural productivity.
  • Deep learning offers automated solutions for plant disease recognition.
  • Existing methods often struggle with simultaneous detection of multiple diseases on a single leaf.

Purpose of the Study:

  • To develop a deep learning model capable of simultaneously detecting and recognizing multiple plant diseases from the same leaf.
  • To enable disease symptom isolation for independent recognition, regardless of other diseases or crop types.
  • To calculate disease prevalence rates and overall disease extent on a leaf.

Main Methods:

  • A novel deep learning-based model was designed for simultaneous multi-disease detection.
Keywords:
AgricultureDeep learningDiseases recognitionLeaf splittingMulti-disease

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  • An isolation method was employed to recognize individual disease symptoms in small leaf regions.
  • The model was trained and tested on a modified PlantVillage dataset using three Convolutional Neural Network (CNN) architectures: Small Inception, MiniVGGNet, and LeNet5.
  • Class weights were utilized to address class imbalances within the dataset.

    • Main Results:

    • The Small Inception CNN architecture demonstrated superior classification performance compared to MiniVGGNet and LeNet5.
    • The proposed isolation method allowed for effective generalization to new crops not seen during training.
    • The model successfully calculated disease prevalence rates and overall disease extent.
    • The use of class weights significantly improved model performance despite dataset imbalances.

    Conclusions:

    • The developed deep learning model effectively detects and recognizes multiple plant diseases simultaneously from a single leaf.
    • The isolation method enhances model generalizability and allows for detailed disease assessment.
    • The approach shows significant potential for application in diverse agricultural settings, though further validation in real-field conditions is recommended.