Plant Disease Detection and Monitoring Using Artificial Neural Network

https://doi.org/10.18535/ijsrm/v10i1.ec01
plant disease, artificial neural network, backpropagation algorithms

Authors

  • Stella I. Orakwue Electrical/Electronic Engineering Department, University of Port Harcourt, Nigeria., Nigeria
  • Nkolika O. Nwazor Electrical/Electronic Engineering Department, University of Port Harcourt, Nigeria, Nigeria
Vol. 10 No. 01 (2022)
Engineering and Computer Science
January 3, 2022

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Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.