A classification model based on depthwise separable convolutional neural network to identify rice plant diseases

Md. Sazzadul Islam Prottasha, Sayed Mohsin Salim Reza

Abstract


Every year a number of rice diseases cause major damage to crop around the world. Early and accurate prediction of various rice plant diseases has been a major challenge for farmers and researchers. Recent developments in the convolutional neural networks (CNNs) have made image processing techniques more convenient and precise. Motivated from that in this research, a depthwise separable convolutional neural network based classification model has been proposed for identifying 12 types of rice plant diseases. Also, 8 different state-of-the-art convolution neural network model has been fine-tuned specifically for identifying the rice plant diseases and their performance has been evaluated. The proposed model performs considerably well in contrast to existing state-of-the-art CNN architectures. The experimental analysis indicates that the proposed model can correctly diagnose rice plant diseases with a validation and testing accuracy of 96.5% and 95.3% respectively while having a substantially smaller model size.


Keywords


agriculture; convolutional neural network; deep learning; image processing; plant disease; rice plant diseases;

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v12i4.pp3642-3654

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578