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Web enabled paddy disease detection using Compressed Sensing.

T Gayathri Devi1, A Srinivasan1, S Sudha1

  • 1Department of ECE, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India.

Mathematical Biosciences and Engineering : MBE
|November 9, 2019
PubMed
Summary

This study introduces a web-based system for detecting paddy plant diseases using Compressed Sensing. The novel approach accurately identifies diseases, aiding farmers in increasing rice yield.

Keywords:
Statistical Gray Level Co-occurrence MatrixArduinoCompressed SensingK-Means clusteringSupport Vector Machineepithelial-mesenchymal transition

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Image Processing

Background:

  • Paddy diseases significantly impact agricultural economics, necessitating efficient detection methods.
  • Early disease identification is crucial for improving rice yield and supporting farmers.

Purpose of the Study:

  • To propose a novel web-based system for paddy disease detection and classification.
  • To enhance storage efficiency using Compressed Sensing for paddy leaf images.
  • To provide a practical solution for farmers to manage crop health.

Main Methods:

  • Image pre-processing including contrast enhancement and LAB color space application.
  • K-Means clustering for image segmentation and Compressed Sensing for storage reduction.
  • Feature extraction using Statistical Gray Level Co-occurrence Matrix (GLCM) and disease classification with Support Vector Machine (SVM).

Main Results:

  • The proposed Compressed Sensing-based system achieved a high disease recognition rate of 98.38%.
  • The system demonstrated reduced storage complexity compared to traditional methods.
  • Experimental validation was performed using an Arduino board for practical application.

Conclusions:

  • The developed web-based system offers an effective and efficient solution for paddy disease detection.
  • Compressed Sensing integration significantly improves storage efficiency in agricultural image analysis.
  • The high accuracy and practical implementation pave the way for improved crop management and increased rice production.