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Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model.

Ghazanfar Latif1,2, Sherif E Abdelhamid3, Roxane Elias Mallouhy1

  • 1Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia.

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Summary

This study introduces a Deep Convolutional Neural Network (DCNN) using transfer learning to detect rice leaf diseases. The advanced system accurately identifies six conditions, improving crop management and yield potential.

Keywords:
VGG19convolutional neural networksdeep learningplant leaf disease detectionrice leaf disease detectiontransfer learning

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Rice is a vital global food source, but crop diseases cause significant yield losses (20-40%).
  • Manual disease detection is impractical and costly for farmers, impacting rice production and consumer prices.
  • Integrating drone technology with machine learning offers a scalable solution for early disease identification.

Purpose of the Study:

  • To develop and evaluate a Deep Convolutional Neural Network (DCNN) transfer learning model for accurate rice leaf disease detection.
  • To classify six distinct rice leaf conditions, including healthy and various diseases like brown spot and leaf blast.
  • To improve the efficiency and accuracy of disease diagnosis in rice cultivation.

Main Methods:

  • A modified VGG19-based transfer learning approach was employed for the DCNN model.
  • The system was trained and tested on a dataset of rice leaf images.
  • Image augmentation techniques were utilized to enhance dataset diversity and model robustness.

Main Results:

  • The proposed DCNN model achieved a highest average accuracy of 96.08% on a non-normalized augmented dataset.
  • Excellent performance metrics were recorded: precision (0.9620), recall (0.9617), specificity (0.9921), and F1-score (0.9616).
  • The modified approach demonstrated superior results compared to existing methods on similar datasets.

Conclusions:

  • The DCNN transfer learning model provides an effective and accurate solution for identifying multiple rice leaf diseases.
  • This technology can significantly aid farmers in early disease detection, potentially reducing crop loss and increasing yield.
  • The developed system offers a cost-effective and scalable method for precision agriculture in rice farming.