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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies

Author 1: Ronnel R. Atole Author 2: Daechul Park
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 1 · Published 2018 · Cited by 136

DOI: https://doi.org/10.14569/IJACSA.2018.090109

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Abstract

This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. A three-class classifier was implemented representing normal, unhealthy, and snail-infested plants via transfer learning from an AlexNet deep network. The network achieved an accuracy of 91.23%, using stochastic gradient descent with mini batch size of thirty (30) and initial learning rate of 0.0001. Six hundred (600) images of rice plants representing the classes were used in the training. The training and testing dataset-images were captured from rice fields around the district and validated by technicians in the field of agriculture.

Keywords

Deep neural network convolutional neural network rice transfer learning AlexNet

How to Cite this Article

Atole, R. R., & Park, D. (2018). A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/10.14569/IJACSA.2018.090109

Atole, Ronnel R., and Daechul Park. "A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies." International Journal of Advanced Computer Science and Applications, vol. 9, no. 1, 2018, https://doi.org/10.14569/IJACSA.2018.090109.

@article{Atole2018,
  title     = {A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Ronnel R. Atole and Daechul Park},
  doi       = {10.14569/IJACSA.2018.090109},
  url       = {https://doi.org/10.14569/IJACSA.2018.090109}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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