Open AccessPlant Disease Detection and Monitoring Using Artificial Neural Network
Author(s) -
Stella Ifeoma Orakwue,
Nkolika O. Nwazor
Publication year - 2022
Publication title -
international journal of scientific research and management
Language(s) - English
Resource type - Journals
ISSN - 2321-3418
DOI - 10.18535/ijsrm/v10i1.ec01
Subject(s) - artificial neural network , hyperspectral imaging , toolbox , backpropagation , matlab , computer science , artificial intelligence , machine learning , plant disease , pattern recognition (psychology) , microbiology and biotechnology , biology , programming language , operating system
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%.