International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 4, August 2022, pp. 3642~3654 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp3642-3654  3642 Journal homepage: http://ijece.iaescore.com A classification model based on depthwise separable convolutional neural network to identify rice plant diseases Md. Sazzadul Islam Prottasha, Sayed Mohsin Salim Reza Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh Article Info ABSTRACT Article history: Received Jan 17, 2021 Revised Dec 19, 2021 Accepted Jan 25, 2022 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 This is an open access article under the CC BY-SA license. Corresponding Author: Md. Sazzadul Islam Prottasha Department of Information and Communication Technology, Bangladesh University of Professionals Mirpur Cantonment, Dhaka-1216, Bangladesh Email: 19541026@bup.edu.bd 1. INTRODUCTION Agricultural science has an enormous effect on the food production system around the world, hence this field is emerging day by day. Technologies have brought a new dimension to this field. Researchers are implementing different methodologies and invented different types of seeds, treatments and weeds to improve the overall crop production. Developments of recent deep learning based image processing methods have improved the disease classification accuracy significantly. Inspired by that, our research is primarily focused on the categorization of various rice plant diseases using a deep learning approach. Contributing to this field has developed a profound interest in us. There are more than 40 different types of rice plant diseases that can be fatal to the rice plants as described by Ou [1]. Diseases like rice blast, smut and leaf blight can cause severe damage to rice production. There exists some other diseases that can be lethal unless necessary measurements are taken early. Researchers have come up with numerous methodologies and models for the detection of rice plant diseases over the years. Different kinds of segmentation and feature extraction methods have been implemented. A multistage convolutional neural network architecture has been presented by Lu et al. [2] that can identify 10 different rice plant diseases. A total of 500 images has been considered including healthy and diseased images and trained using the convolutional neural network (CNN) model. The result reported in the paper shows an accuracy of 95.38% while diagnosing the rice plant diseases. The work was conducted on 10 types of rice plant diseases, however there are only 500 training images meaning only 50 images per disease class. Based on the minimal quantity of training pictures, it seems doubtful that this model will be viable in