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Related Experiment Videos

A deep learning optimized model for classification and detection of rice leaf diseases.

Shashank Chaudhary1, Upendra Kumar2, Biswa Mohan Sahoo3

  • 1AKTU Lucknow, Lucknow, Uttar Pradesh, India.

Scientific Reports
|July 3, 2026
PubMed
Summary

Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Accurate rice leaf disease diagnosis is crucial for food security. A novel deep learning model, ROA-DM, utilizing the remora optimization algorithm (ROA), achieved 98.5% accuracy in identifying rice plant diseases.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Plant diseases, particularly rice diseases, pose a significant threat to global food security by impacting food productivity, quantity, and quality.
  • Accurate and timely diagnosis of rice leaf diseases is essential for effective disease management and mitigation strategies.

Purpose of the Study:

  • To develop and evaluate a deep learning model for the accurate categorization and forecasting of rice plant diseases.
  • To enhance the performance of deep learning models for plant disease classification using an optimization algorithm.

Main Methods:

  • The study employed a deep learning framework integrating a deep maxout network (DMN) and a deep autoencoder (DAE).
  • The remora optimization algorithm (ROA) was utilized to optimize the learning parameters of the deep model, aiming for improved convergence and avoidance of local minima.
Keywords:
DAEDMNDeep learning modelRemora optimization algorithmRice leaf disease classification

Related Experiment Videos

  • The proposed ROA-DM method was applied to a rice leaf dataset for disease detection and classification.

    • Main Results:

    • The ROA-DM method demonstrated high accuracy and precision across various rice disease categories.
    • Experimental results, including confusion matrices, showed strong training and validation performance.
    • The optimized learning approach achieved a classification accuracy of 98.5% for rice leaf diseases.

    Conclusions:

    • The developed ROA-DM deep learning model shows significant potential for accurate and precise identification of rice leaf diseases.
    • The integration of the remora optimization algorithm enhances the performance of deep learning models in plant disease classification.
    • This approach contributes to improving disease management practices, thereby supporting food security.