A classification model based on depthwise separable convolutional neural network to identify rice plant diseases
| Autor: | Md. Sazzadul Islam Prottasha, S M SALIM Reza |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Electrical and Computer Engineering (IJECE). 12:3642 |
| ISSN: | 2722-2578 2088-8708 |
| DOI: | 10.11591/ijece.v12i4.pp3642-3654 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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