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September 10, 2025Network Computation in Neural Systems

AI-driven plant disease detection with tailored convolutional neural network

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Authors

SHSk Mahmudul HassanKNKeshab NathMJMichał Jasiński

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Overview

This research demonstrates a genetic algorithm optimizing CNN hyperparameters, achieving high accuracy in plant disease detection.

Key Points

  • The proposed CNN model achieved an accuracy of 97.6% on the tea leaf disease dataset, demonstrating its effectiveness in disease detection.
  • Validation on additional datasets resulted in accuracies of 96.99% and 99%, indicating robust performance across varied datasets.
  • By optimizing hyperparameters with a genetic algorithm, computational efficiency and model performance were significantly improved.
  • Comparative analysis showed the new model outperformed several established deep learning architectures with fewer parameters.

Cite This Study

Hassan et al. (2025) studied this question.

synapsesocial.com/papers/68c1a91354b1d3bfb60e259fhttps://doi.org/10.1080/0954898x.2025.2537680
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Plant Health AI Analyzer Using Supervised Learning2025
  2. 2Enhanced Tea Leaf Disease Detection using Deep Learning2024 · 1 citations
  3. 3An Explainable Deep Learning Framework for Plant Leaf Disease Detection Using a Custom CNN Model2026
  4. 4A Lightweight Deep Learning Model for Tea Leaf Disease Identification2025
  5. 5Smart Crop Disease Using CNN Model2026