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Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between

Farida Siddiqi Prity1, Mirza Raquib2, Saydul Akbar Murad3

  • 1Department of Computer Science and Engineering Netrokona University Netrokona Bangladesh.

Food Science & Nutrition
|December 22, 2025
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Summary

This study introduces a Feature Analysis Detection Model (FADM) for early rice leaf disease detection, outperforming direct image analysis. This AI approach enhances crop health and reduces yield loss for sustainable farming.

Keywords:
Artificial Neural NetworkExtreme Learning MachineFeature Extraction Algorithmdiseaserice

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Rice diseases cause significant yield loss and economic damage.
  • Early detection is crucial for effective disease management and improved crop yields.
  • Current AI approaches often lack comparative analysis of feature extraction methods.

Purpose of the Study:

  • To compare a Feature Analysis Detection Model (FADM) with a Direct Image-Centric Detection Model (DICDM) for rice leaf disease classification.
  • To evaluate the effectiveness of various Feature Extraction Algorithms (FEAs), Dimensionality Reduction Algorithms (DRAs), and Feature Selection Algorithms (FSAs).
  • To investigate the application of Extreme Learning Machine (ELM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in rice disease detection.

Main Methods:

  • Experiments were conducted on a dataset of 3829 rice leaf images across six classes.
  • The FADM utilized various FEAs, DRAs, FSAs, and ELM.
  • A DICDM was implemented without FEAs for comparative analysis.
  • Classification performance was evaluated using multiple metrics, and Grad-CAM was used for interpretability.

Main Results:

  • The Feature Analysis Detection Model (FADM) achieved the highest classification performance.
  • The study provides a comprehensive comparison between FADM and DICDM.
  • Grad-CAM visualization confirmed model interpretability.

Conclusions:

  • The proposed FADM demonstrates superior performance in classifying rice leaf diseases compared to direct image analysis.
  • This AI-driven approach offers significant potential for improving rice crop health and sustainability.
  • Accurate early detection can minimize economic losses and enhance agricultural productivity.