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EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction.

C Ashwini1, V Sellam1

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu India.

Neural Computing & Applications
|May 8, 2023
PubMed
Summary

This study introduces a 3D-dense convolutional neural network (3D-DCNN) enhanced by the Ebola optimization search (EOS) algorithm for accurate corn disease prediction. The novel approach improves prediction accuracy and classification effectiveness in agriculture.

Keywords:
3D-dense convolutional neural network (3D-DCNN)Agricultural productivityContext-aware attentionCorn disease predictionEbola optimization search (EOS) algorithm

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Corn disease significantly impacts agricultural productivity and food security.
  • Existing AI methods for corn disease prediction often face challenges with insufficient data and suboptimal accuracy.
  • Effective disease prediction is crucial for timely intervention and yield optimization.

Purpose of the Study:

  • To develop and evaluate a novel 3D-dense convolutional neural network (3D-DCNN) model optimized with the Ebola optimization search (EOS) algorithm for corn disease prediction.
  • To enhance prediction accuracy and classification effectiveness compared to conventional AI techniques.
  • To address the challenge of insufficient dataset samples through pre-processing and optimization.

Main Methods:

  • A 3D-dense convolutional neural network (3D-DCNN) architecture was employed for feature extraction and classification.
  • The Ebola optimization search (EOS) algorithm was utilized to optimize the 3D-DCNN, reducing classification errors.
  • Preliminary data pre-processing techniques were applied to augment the dataset and improve sample quality.
  • Model performance was evaluated using metrics such as accuracy, AUC, F1 score, and RMSE in the MATLAB 2020a environment.

Main Results:

  • The proposed 3D-DCNN-EOS model demonstrated significantly improved accuracy in corn disease prediction.
  • The EOS optimization effectively reduced classification errors, leading to more effectual disease prediction and classification.
  • The model outperformed existing techniques across various performance metrics, including precision, AUC, F1 score, KSE, accuracy, RMSE, and recall.
  • Feature representation learning was enhanced, contributing to the model's superior performance.

Conclusions:

  • The 3D-DCNN-EOS model offers a highly accurate and effective solution for corn disease prediction.
  • The integration of EOS algorithm optimization addresses data scarcity and enhances the robustness of deep learning models in agriculture.
  • This approach represents a significant advancement in applying AI for intelligent agricultural management and disease surveillance.