\documentclass{iaesarticle}

%%required package. add for your convenient, but do not remove the initial
\setlength{\headsep}{0.15in}
\usepackage{amsmath, amsfonts, amssymb, float, fancyhdr}
\usepackage[figuresright]{rotating}
\usepackage{authblk, graphicx, indentfirst, lastpage, lipsum}
\usepackage{pifont}
\usepackage{color}
\usepackage{multirow}
\usepackage{multicol}
\renewcommand{\Authsep}{, }
\renewcommand{\Authand}{, }
\renewcommand{\Authands}{, }
\setlength{\affilsep}{0cm}
\renewcommand\Authfont{\normalsize}
\renewcommand\Affilfont{\normalfont\small}
\makeatletter
\renewcommand{\@biblabel}[1]{[#1]\hfill}
\makeatother
\usepackage{subfig, caption, epstopdf}
\usepackage{hyperref}
\usepackage[left=3cm, right=2.5cm, top=2.5cm, bottom=2.5cm, includehead, includefoot]{geometry}
\usepackage[justification=centering]{caption}
\captionsetup{labelsep=period}
\usepackage{titlesec}
\usepackage{xcolor}
\titleformat{\section}
{\normalfont\normalsize\bfseries\uppercase}{\thesection}{1.7em}{}
\titleformat{\subsection}
{\normalfont\normalsize\bfseries}{\thesubsection}{.95em}{}
\titleformat{\subsubsection}
{\bfseries}{\thesubsubsection}{.2em}{}
\titlespacing*{\section}{0cm}{0.7cm}{0cm}
\usepackage{enumitem}
\usepackage[numbers,compress]{natbib}




%%leave copyright info to the editor
\CopyrightLine[]{}{\textit{This is an open access article under the \textcolor{blue}{\underline{CC BY-SA}} license.}
\vspace{.5em}}
\raggedbottom

%%author
\author[1]{\bfseries Md. Sazzadul Islam Prottasha}
\author[2]{\bfseries S M Salim Reza}


%%author's affiliation
\affil[1,2]{Department of Information and Communication Technology, Bangladesh University of Professionals, Bangladesh}


%%title and shortitle (for footer)
\title{A Classification Model based on Depthwise Separable Convolutional Neural Network to Identify Various Rice Plant Diseases}
\shorttitle{Depthwise Separable CNN to Identify Various Rice Plant Diseases (Md. Sazzadul Islam Prottasha)}

%%starting
\begin{document}
\setcounter{page}{1}

%%indentation. do not change
\setlength{\parindent}{1.27cm}

%%header and footer setting. do not change
\pagestyle{fancy}
\fancyhfoffset{0cm}

%%journal info
\journalname{International Journal of Electrical and Computer Engineering (IJECE)}
\journalshortname{Int J Elec \& Comp Eng}
\journalhomepage{http://ijece.iaescore.com}
\vol{x}
\no{x}
\months{August}
\years{202x}
\issn{2088-8708}
\DOI{10.11591}
\pagefirst{xx}
\pagelast{xx}

%%build title
\maketitle

%%border setting. do not change
\hrule
\vspace{.1em}
\hrule
\vspace{.5em}
\noindent
\parbox[t][][s]{0.275\textwidth}{%
\textbf{Article Info}
\vspace{.5em}
\hrule
\vspace{.5em}
\begin{history}
\vspace{.5em}

%%article info. editor's privilege
Received Jan 17, 2021

Revised Jun x, 2021

Accepted XXX xx, 2021

\vspace{.7em}
\end{history}
\vspace{.5em}
\hrule
\vspace{.5em}
\begin{keyword} 
\vspace{.5em}
%%write keyword here. separate by \sep
Convolutional neural network \sep Deep learning \sep Depthwise separable convolution \sep Rice disease identification \sep Plant disease
\vspace{.5em}
\end{keyword}
\vspace{\fill}
}
\parbox{0.025\textwidth}{\hspace{0.5em}}
\parbox[t][][s]{0.7\textwidth}{%
\begin{abstract}
\vspace{.3em}
%% Text of abstract
Every year a number of rice diseases cause major damage to crops 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 (CNN) 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 comparison with the other state-of-the-art CNN architectures. The experimental result indicates that the proposed model can identify the rice plant diseases with a validation and testing accuracy of 96.5\% and 95.3\% respectively while having a substantially smaller model size. 
\end{abstract}
}
\parbox[l]{\textwidth}{%
\rule{0.275\textwidth}{0.5pt} \hspace{0.5cm} \hrulefill
\\
\emph{\textbf{Corresponding Author:}}
\vspace{.5em}\\
%% correspondence info. separate by \\
Md. Sazzadul Islam Prottasha,\\
Department of Information and Communication Technology,\\
Bangladesh University of Professionals,\\
Mirpur Cantonment, Dhaka-1216, Bangladesh.\\
Email: sazzadulislamprottasha@gmail.com
}
\vspace{.5em}
\hrule
\vspace{.1em}
\hrule


%% main text

\section{Introduction}
\label{}
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. Our work is mainly focused on the classification of various rice plant diseases using a deep learning approach and providing some information regarding known treatments of the detected diseases. Contributing to this field has developed a profound interest in us.

There are more than 40 types of rice plant diseases that can cause significant damage to the rice plant as described by Ou [1]. Diseases like rice blast, smut and leaf blight can cause severe damage to rice production. There are some other diseases that can be lethal unless necessary measurements are taken early. Over the years different researcher has proposed several methods and models in detecting rice plant diseases. Different kinds of segmentation and feature extraction methods have been implemented. The work by Lu \textsl{et al.} [2] proposed a multistage Convolutional Neural Network architecture for detecting 10 different rice plant diseases. A total of 500 images including healthy rice leaves and diseased images has been considered and trained using the CNN model. The proposed CNN model consists of three convolution layers, three stochastic pooling layers and two fully connected layers with a softmax activation function. With 10-fold cross validation, the proposed model achieved an accuracy of 95.38\%. 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. It is doubtful that this model will work well in real-life scenarios considering the small amount of training images. The work by Liu \textsl{et al.} [3] proposed a machine learning based and a CNN based model for identifying rice false smut images. They proposed a CNN architecture inspired by AlexNet and VGGNet-16 architecture having a total of 10 layers which includes 7 convolution layers and 3 fully connected layers. Experimental result suggests that the proposed method performed better than AlexNet in detecting the rice false smut disease. The work by Mohan \textsl{et al.} [4] proposed a two-stage method for identifying rice plant diseases where in the first stage, they identified the disease affected area by using Haar-like features and AdaBoost classifier. Once the disease affected area was identified, they used Scale Invariant Feature Transform for the feature extraction process. Finally, SVM and KNN classifiers were used for detecting rice plant diseases. Ihsan \textsl{et al.} [5] proposed an image processing approach to identify bacterial leaf blight and brown spots in rice plants. The image segmentation was done using K-means clustering and the plant textures are extracted using the Gray Level Co-Occurrence Matrix (GLCM). The work by Shunmugam and Dharmar [6] proposed an optimized neural network to detect rice plant diseases. They took samples of 4 different types of diseases namely rice blast, bacterial blight, sheath rot and brown spot. Initially, the RGB images were converted into HSV images and analyzing the hue and saturation they extracted binary images from the hue and saturation difference. This work introduced Jaya optimization algorithm in collaboration with the neural network. Jaya algorithm has been used to update the weights of the proposed neural network in identifying rice diseases. The work by Krishnamoorthy \textsl{et al.} [7] used InceptionResNetV2 model for identifying various rice leaf diseases. 
By using transfer learning method and tuning the hyperparameters the proposed model acheived an accuracy of 95.67\%. The method provided by Shrivastava \textsl{et al.} [8] uses the AlexNet model using transfer learning to detect rice plant diseases namely Rice blast, Leaf blight and Sheath blight. For an 80-20 train and test data split, the AlexNet model achieved a classification accuracy of  91.37\%. A simple CNN model for identifying various rice plant diseases from Bangladesh has been proposed at [9]. The work focuses on providing a lightweight CNN model by hyper-parameter tuning which can provide admissible accuracy. The statistical analysis shows that using Adam optimizer their proposed model achieved a mean validation accuracy of 95.4\%. Similar CNN model has been proposed for tomato [10], wheat [11], cotton [12], apple [13], peach [14].

Recent developments in various computer vision models have significantly improved the accuracy of object detection modules. Architectures like Faster R-CNN , YOLOv3, Mask R-CNN, RetinaNet etc. are being used for detecting various plant diseases. These models not only detect the disease but also identifies the exact location of the disease occurrence. The work by Zhou \textsl{et al.} [15] proposed a k-means clustering algorithm combined with Faster R-CNN to detect rice diseases. For identifying the rice diseases from various complex heterogeneous backgrounds Otsu threshold segmentation algorithm was used. Finally, the k means clustering algorithm was combined with the Faster R-CNN to identify the diseases. The result reported in the paper suggests that combining the FCM-KM with Faster RCNN provides better accuracy than Faster R-CNN alone. Not only the accuracy but also the execution time was reduced. The work by Sethy \textsl{et al.} [16] proposed a faster R-CNN object detection method for rice false smut disease. Initially, they collected 50 images of false smut and used image augmentation methods to increase the images in the dataset. They used the ResNet-50 model to extract the features of the images with 1 minibatch and 5 epochs. Once the features are extracted the outputs are fed through the fully connected layer to predict the bounding boxes. However, if there’s multiple false smut present, the model sometimes fails to identify all of the smut using bounding boxes. Bari \textsl{et al.} [17] proposed a faster R-CNN based real-time rice leaf disease diagnosis system. By taking public dataset images along with their own collected images they formulated a dataset for rice blast, brown spot and hispa diseases. The proposed method can localize the disease-affected portions with higher accuracy.
However, the model requires multiple recurrences to extract all objects inside a single image which makes the model slower.

\section{Materials and Methodology}
\label{}
In this section, the details of the dataset collection process and proposed models are discussed in appropriate subheadings. 
\subsection{Dataset Collection}
The first step of our research work is data acquisition. We have taken some extensive measurements to collect a total of 1677 rice plant images from several rice fields of Bangladesh consisting of 12 types of fungal and pest diseases along with healthy plants. The data were collected under different weather conditions from December 2019 to March 2020 for a total period of 4 months. Along with our collected images, we have taken some more images from the dataset stated by Rahman \textsl{et al.} [18]. The images were taken on different heterogeneous backgrounds under different lighting conditions both in sunny and overcast environments. The collected types of diseases we contemplated are bacterial blight, brown plant hopper, brown spot, false smut, hispa, leaf blast, neck blast, leaf scald, leaf smut, sheath blight rot, stemborer and stackburn. We also collected images of healthy leaves and panicles to distinguish them from the disease-affected plants. The dataset can be found in this\href{http://shorturl.at/jnM15}{ google drive}. 
Figure 1 shows the collected sample images of our dataset. 

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.27]{dataset.pdf}}
\vspace{.7em}
\caption{Sample dataset images }
\label{fig}
\end{figure}

The images were captured during different stages of the disease infection considering different symptoms of the diseases; hence we get a fully representative dataset considering all aspects of the disease. Table 1 shows the number of sample images collected for different symptoms of diseases and pests.

% Please add the following required packages to your document preamble:
% \usepackage{multirow}
\begin{table}[]
\caption{Total Collected Sample Images With Different Symptom Variations}
\renewcommand{\arraystretch}{1.1}
\centering
\fontsize{9pt}{10pt}\selectfont
\begin{tabular}{|c|c|c|c|}
\hline
\multicolumn{1}{|c|}{\textbf{Name}} & \textbf{Sample Image Variation}       & \multicolumn{1}{c|}{\textbf{\begin{tabular}[c]{@{}c@{}}No. of \\ Collected Images\end{tabular}}} & \multicolumn{1}{c|}{\textbf{Total Images}} \\ \hline
\multirow{2}{*}{Bacterial Blight}   & Early stage yellowish brown           & 46                                                                                               & \multirow{2}{*}{126}                \\ \cline{2-3}
                                    & Later stage full brown                & 80                                                                                               &                                     \\ \hline
\multirow{2}{*}{Brown Plant Hopper} & Early stage of attack                 & 50                                                                                               & \multirow{2}{*}{75}                 \\ \cline{2-3}
                                    & Late stage of attack                  & 25                                                                                               &                                     \\ \hline
Brown Spot                          & Small size spots                      & 102                                                                                              & 102                                 \\ \hline
\multirow{2}{*}{False Smut}         & Black smut                            & 78                                                                                               & \multirow{2}{*}{112}                \\ \cline{2-3}
                                    & Brown smut                            & 34                                                                                               &                                     \\ \hline
\multirow{2}{*}{Hispa}              & Visible pests on the leaves           & 56                                                                                               & \multirow{2}{*}{86}                 \\ \cline{2-3}
                                    & Visible spots on the leaves, no pests & 20                                                                                               &                                     \\ \hline
\multirow{2}{*}{Leaf Blast}         & Deep brown spot                       & 110                                                                                              & \multirow{2}{*}{136}                \\ \cline{2-3}
                                    & Yellowish grey spot                   & 26                                                                                               &                                     \\ \hline
Neck Blast                          & Blast spot on neck                    & 225                                                                                              & 225                                 \\ \hline
\multirow{2}{*}{Leaf Scald}         & Dark brown lesion                     & 23                                                                                               & \multirow{2}{*}{73}                 \\ \cline{2-3}
                                    & Greyish white lesion                  & 50                                                                                               &                                     \\ \hline
Leaf Smut                           & Spots on the whole leaf               & 103                                                                                              & 103                                 \\ \hline
\multirow{2}{*}{Sheath Blight}      & Black stem                            & 112                                                                                              & \multirow{2}{*}{228}                \\ \cline{2-3}
                                    & White stem                            & 116                                                                                              &                                     \\ \hline
\multirow{2}{*}{Stemborer}          & Grain symptom                         & 171                                                                                              & \multirow{2}{*}{187}                \\ \cline{2-3}
                                    & Stem symptom                          & 16                                                                                               &                                     \\ \hline
\multirow{2}{*}{Stackburn}          & Symptom on whole leaf                 & 49                                                                                               & \multirow{2}{*}{71}                 \\ \cline{2-3}
                                    & Symptom on a part                     & 22                                                                                               &                                     \\ \hline
\multirow{2}{*}{Healthy}            & Leaf                                  & 90                                                                                               & \multirow{2}{*}{153}                \\ \cline{2-3}
                                    & Grain                                 & 76                                                                                               &                                     \\ \hline
\end{tabular}

\end{table}

Bacterial leaf blight caused by a bacteria called Xanthomonas oryzae pv. oryzae which attacks the leaves of the rice plant causing it to dry out [19]. Brown spot is a fungal disease caused by Cochliobolus miyabeanus. This disease mostly occurs at the protective sheath covering the leaf of the rice plants, leaves and spikelets [20]. False smut is a rice grain disease caused by a plant pathogen named Ustilaginoidea virens which transforms the individual rice grains into yellow fruiting bodies [21]. In some cases, the smuts turn black in color. We have considered both types of false smut images in our dataset. Leaf blast, neck blast, leaf scald, leaf smut, sheath blight and stackburn are similar types of disease considering the fact that they mostly occur at the rice leaf. Each of the diseases causes small spots or lesions in the rice plants. In different cases, there can be single or multiple spots with similar diameter in a single leaf. Neck blast also attacks the neck calm of the rice plant making it vulnerable. Leaf blast and neck blast are more deadly than the other diseases and in severe cases, the yield loss can be as high as 100\% as indicated in [22]. 

We have also considered some pests in our dataset. Brown planthopper is a disease caused by a pest named Nilaparvata lugens. The pests mostly reside at the root of the plants then spreading through the whole plant causing the plant to dry out and turn brown [23]. Hispa is another disease that is caused by a pest named Dicladispa armigera where the armigera insect scrapes the upper surface of the leaves leaving only the lower epidermis. Similar to hispa, stemborer is a pest that also attacks the leaves of rice plants. The stem borer initially resides at the base of the rice plants and later the stem borer larva drill through the upper nodes and feeds the tillers which cause the tillers to dry out [24]. Different environmental factors along with excessive use of nitrogen fertilizers are the main reason for these disease occurrences.


\subsection{Image Pre-processing}
After collecting the rice plant sample images, we processed the sample images before inputting them to the convolutional neural network model. Firstly, we discarded the blurry and out-of-focus images from the dataset. Once the faulty images were discarded, we move on to the image augmentation process to increase the size of our dataset. Since our dataset consists of a small number of images for each class, hence for better interaction and considering all possible dependencies at the feature level, we perform image augmentation. The first augmentation method we carried out is the contrast stretching method. Due to the overcast environment and mobile camera lens, some of the sample images had very low contrast, hence making it difficult to distinguish between the foreground subject and the background. This type of low contrasting images will lead to false positive and false negative rates in the network resulting in low accuracy. Therefore, we used the Contrast Limited Adaptive Histogram Equalization method described by Pizer \textsl{et al.} [25] to enhance the low contrast sample images. We performed local histogram enhancement using the CLAHE method. CLAHE limits the amplification by clipping the histogram at a predefined value before calculating the Cumulative Distribution Function. The clipping value depends upon the normalization of the histogram hence on the size of the local area.
Considering probabilistic value of histogram assuming all pixels $N$ with intensity $x_i$ as drawings from a stochastic $X$. We can express the occurrence of one value of $k$ as a probability in equation 1,
\begin{equation}
P_x(k)=\ P(X=k)=\frac{Number\ of\ pixel\ with\ value\ k}{Total\ number\ of\ pixel}\ =\ \frac{\sum_{i=1}^{N}{I{x_i=k}}}{N}\
\end{equation}
Here, $I\{x_i=k\}$ is the indicator function where, $I =\begin{cases}1 & x_i = k\\0 & x_i \neq  k\end{cases} $

The Cumulative Distribution Function (CDF) is defined as,
\begin{equation}
    F_x(k)=\sum_{i=1}^{k}{P_x(k)}
\end{equation}

This CDF is uniformly distributed in a small area of the input images on a range [0,255], therefore the transformation y defined as,
\begin{equation}
y=F_x(k)\ \times\ 255\ =\ \sum_{i=1}^{k}{P_x(k)}\ \times\ 255
\end{equation}

Thus, the CDF is computed over the pixels of the images. After applying the CDF, bi-linear interpolation is performed to remove any artifacts in tile borders. Figure 2 shows the result of histogram equalization on an input image. It is evident that the intensity values of histogram equalized images are spread through the entire dynamic range hence the details are much more comprehended. 

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.18]{histogram equalization.pdf}}
\vspace{.7em}
\caption{Contrast Limited Adaptive Histogram Equalization method  }
\label{fig}
\end{figure}



After performing the histogram equalization method, we performed various image augmentation methods on our dataset. We performed flipping, skewing, random rotation, noise and distortion, shear transformation and random zoom as different image augmentation processes. Using these procedures, 10 augmented images for each sample image have been generated and thus our dataset increased to a total of 16770 training images. We used 80-20 data split on our dataset by which we have 13415 and 3355 images for training and validating the model respectively.

\subsection{State-of-the-art CNN architectures}
We used 8 different state-of-the-art CNN architectures on our rice disease dataset based on different criteria. We used VGG net which has 3×3 filters throughout the CNN architecture and they are very deep CNN architectures with a large number of parameters [26]. The Inception v3 architecture by Szegedy \textsl{et al.} [27] has an inception module. This architecture is also very deep but using the inception module the parameter size is reduced significantly. By the use of asymmetric convolution, factorization is performed which reduces the complexity of this model. ResNet architecture has residual networks with skip connections and overcomes the problem of overfitting in imagenet dataset [28]. The Xception [29] introduced depthwise separable convolutions along with residual connections. Mobilenet v2 architecture also uses depthwise separable convolutions having inverted shortcut network with linear bottleneck [30]. NasNet Mobile architecture is similar to Mobilenet v2 architecture and it is used primarily for mobile devices. We intended to see how our dataset performs on these models and later compared the results of these state-of-the-art CNN models with our proposed CNN model. 

\subsection{Proposed depthwise CNN model}
We proposed a deep CNN architecture based on depthwise separable convolution. Our proposed CNN model starts with a 3×3 2D convolution layer followed by batch normalization and max-pooling layer. In the 2nd layer, we used a depthwise separable convolution layer which consists of a 3×3 depthwise convolution followed by a 1×1 point-wise convolution. Unlike the normal convolution, depthwise separable convolution deals with not only the spatial dimension but also the depth dimension.

For normal convolution considering a kernel $K$ having spatial height $K_h$ and spatial width of  $K_w$ and image tensor with input and output channels of $I_i$ and $I_o$ respectively, then the convolution layer is represented by, $C \in \mathbb{R}\ ^{I_i\ \times I_o\ \times K_h\ \times K_w}$. After applying the filter to a 3D tensor $x$, with size ${\ K_h\times K}_w\times\ I_i\ $, we obtain a response vector $y \in \mathbb{R}\ ^{I_o}$
\begin{equation}
\label{eqn_dbl_4}
y\ =\ C\ \ast\ x
\end{equation}



where $y_{o} = \sum_{i=1}^{I_i} C_{o,i} * x_i, o\in I_o, i\in I_i$, Here $\bigm\ast$ is the convolution operator. $C_{o,i} = C\left [ o,i,:,: \right ]$ is a tensor patch along the \textit{i-th} input channel and \textit{o-th} output channel.  $x_{i} = x\left [ i,:,: \right ]$ is a tensor patch along the \textit{i-th} input channel of 3D tensor x. 

For depthwise separable convolution, the convolutions are performed at each depth by which the output is split for each dimension. Then pointwise convolution is performed for projecting the outputs into a single channel space. Pointwise convolution is done by performing 1×1 cross-channel convolutions. Since the depthwise convolution is performed at each depth at a time hence the kernel tensor is $C_d \in \mathbb{R}\ ^{I_i\times 1\times K_h\times\ K_w}$ and for pointwise convolution the kernel tensor is $C_p\in \mathbb{R}\ ^{I_i \times\ I_o \times1\times1}$. After applying this to a 3D tensor $x$, we obtain a response vector ${y}'$ as

\begin{equation}
\label{eqn_dbl_5}
{y}\prime\ \ =\ (C_p\ \circ C_d)\ \ast\ x
\end{equation}



Where $y_{o}' = \sum_{i=1}^{I_i} C_p_{(o,i)} (C_d_{(i)}* x_i), \circ$ is a element wise operation. $C_p_{(o,i)} = C_p\left [ o,i,:,: \right ]$ is a tensor patch along the \textit{i-th} input channel and \textit{o-th} output channel and $C_d_{(i)} = C_d\left [ i,:,: \right ]$ is a tensor patch along the \textit{i-th} input channel of 3D tensor $x$. 

As we can see, rather than performing the convolution in spatial dimension in RGB images, depthwise separable convolutions are performed for each color channel separately. Due to single-channel calculation, the overall complexity of the depthwise separable convolution is less than the normal convolution on spatial dimension which accelerates the overall classification process. Because of the small parameter size and faster execution we specifically used depthwise separable convolution in our proposed CNN model. The proposed depthwise CNN architecture is demonstrated in figure 3. 


\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.17]{cnn architecture.pdf}}
\vspace{.1em}
\caption{Proposed depthwise separable CNN model}
\label{fig}
\end{figure}

The network starts with a normal 3×3 convolution followed by 2 depthwise separable convolutions and then again a normal 3×3 convolution. Finally, we used 2 fully connected layers having 128 neurons each and a 0.3 dropout layer in between the dense layer to reduce overfitting. 2×2 max pooling and batch normalization were performed after every convolution. We used the ReLU activation function for each of the layers except for the final dense layer which uses the softmax activation function for the classification problem. We used different loss functions such as Adam, RmsProp, Hingeloss for updating the weights and observed their performance. The hyperparameters of our proposed CNN model are given below.

    
\textbf{Fold:} 10-fold cross validation

\textbf{Minibatch:} 64

\textbf{Epoch:} 100

\textbf{Activation Function:} ReLU

\textbf{Loss Function:} Categorical Cross Entropy

\textbf{Momentum:} 0.9

\textbf{Dropout:} 0.3


All the images were resized to 224×224 pixels before inputting them in the CNN model. The first layer of our CNN model takes an input (224,224,3) and produces an output tensor of (222,222,32). After every convolution, batch normalization and max-pooling are performed. We performed a 2×2 max pooling with a stride of 2. The first max-pooling layer output a tensor of (111,111,32). Then we perform 2 depthwise separable convolutions and one normal convolution layer along with max-pooling layers. After performing all the operations finally we get an output tensor of (12,12,128). Table 2 shows the kernel dimension of each layer along with the generated feature maps of our proposed CNN model.


\begin{table}[htbp]
\caption{Proposed Model Generated Feature Vector Output Of Each Layer}
\renewcommand{\arraystretch}{1.1}
\centering
\fontsize{8pt}{10pt}\selectfont
\begin{tabular}{ccccc}
\hline
\textbf{Layer Name} & \textbf{Kernel Dimension} & \textbf{Stride} & \textbf{Input Feature   Vector} & \textbf{Output Feature   Vector} \\ \hline
Conv\_2d            & 3×3                       & -               & 224×224×3                       & 222×222×32                       \\
Maxpooling1\_2d     & 2×2                       & 2               & 224×224×32                      & 112×112×32                       \\
Separableconv1\_2d  & 3×3                       & -               & 112×112×32                      & 109×109×32                       \\
Maxpooling2\_2d     & 2×2                       & 2               & 109×109×32                      & 54×54×32                         \\
Separableconv2\_2d  & 3×3                       & -               & 54×54×32                        & 52×52×64                         \\
Maxpooling3\_2d     & 2×2                       & 2               & 52×52×64                        & 26×26×64                         \\
Conv3\_2d           & 3×3                       & -               & 26×26×64                        & 24×24×128                        \\
Maxpooling3\_2d     & 2×2                       & 2               & 24×24×128                       & 12×12×128                        \\
Flatten             & -                         & -               & 12×12×128                       & 18432                            \\ \hline
\end{tabular}
\end{table}


The output tensor (12,12,128) is fed through the flatten layer which maps the input matrix into a 1-D vector of 18432 values. This vector is passed through two fully connected layers having 128 neurons and finally, we get 13 probabilistic values and thus the classification is performed.
Figure 4 demonstrates the feature maps generated by the last convolution layer for each disease class.

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.29]{feature map.pdf}}
\vspace{.1em}
\caption{Final convolution layer output for each class}
\label{fig}
\end{figure}




\section{Result and Discussion}
\label{}
In this section, the detailed analysis of various state-of-the-art CNN models performance along with our proposed model and the findings are discussed in appropriate subheadings. 

\subsection{State-of-the-art CNN models Performance}
For experimental work, Keras framework with TensorFlow back-end with Tesla P-100 GPU has been used to train the models. For the training process, we used two different methodology transfer learning and fine-tuning. In the transfer learning method, each of the architectures had pre-trained imagenet weights. Only the final dense layer is trained by randomly initialized weights. Later we used fine-tuning method where we unfreeze the last dense layer of the CNN architecture and freeze all of the other layer weights. Then we trained the dense layer using our rice plant dataset and initialize different weights to the nodes. The networks later adjusted the weights using optimizers. 10-fold cross-validation accuracy has been considered as the model performance metric. Table 3 shows the training and validation result after 100 iterations of the rice plant disease dataset on different state-of-the-art CNN architectures.

% Please add the following required packages to your document preamble:
% \usepackage{multirow}
\begin{table}[htbp]
\caption{Statistical Analysis of Different CNN architectures On Rice Plant Disease dataset. Here Bold Numbers Indicate Best Result}
\centering
\fontsize{9pt}{10pt}\selectfont
\begin{tabular}{cccccc}
\hline
\multicolumn{1}{c}{\textbf{Architecture}} & \multicolumn{1}{c}{\textbf{Training   Method}} & \multicolumn{1}{c}{\textbf{\begin{tabular}[c]{@{}c@{}}Mean Train   \\ Accuracy\end{tabular}}} & \multicolumn{1}{c}{\textbf{\begin{tabular}[c]{@{}c@{}}Mean Validation   \\ Accuracy\end{tabular}}} & \multicolumn{1}{c}{\textbf{\begin{tabular}[c]{@{}c@{}}Mean Test   \\ Accuracy\end{tabular}}} & \multicolumn{1}{c}{\textbf{\begin{tabular}[c]{@{}c@{}}Total   \\ Parameters\end{tabular}}} \\ \hline
\multirow{2}{*}{VGG 16}                     & Transfer learning                               & 94.2±1.8\%                                                                                    & 85.3±1.5\%                                                                                         & 82.2±1.0\%                                                                                   & \multirow{2}{*}{134 million}                                                                \\ 
                                            & Fine Tuning                                     & 98.1±0.9\%                                                                                    & 95.1±0.4\%                                                                                         & 93.1±0.5\%                                                                                  &                                                                                             \\ \hline
\multirow{2}{*}{VGG 19}                     & Transfer learning                               & 89.5±1.5\%                                                                                    & 81.3±3.5\%                                                                                         & 78.1±2.0\%                                                                                   & \multirow{2}{*}{143 million}                                                                \\ 
                                            & Fine Tuning                                     & 97.2±0.8\%                                                                                    & 93.6±0.3\%                                                                                         & 86.2±1.5\%                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{Inception v3}               & Transfer learning                               & 84.8±3.0\%                                                                                    & 65.2±2.5\%                                                                                         & 64.7±3.0\%                                                                                   & \multirow{2}{*}{24 million}                                                                 \\ 
                                            & Fine Tuning                                     & 98.6±0.4\%                                                                                    & 84.1±1.5\%                                                                                         & 83.2±2.0\%                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{Xception}                   & Transfer learning                               & 87.2±2.5\%                                                                                    & 84.2±1.5\%                                                                                         & 79.4±2.0\%                                                                                   & \multirow{2}{*}{20 million}                                                                 \\ 
                                            & Fine Tuning                                     & \textbf{99.1±0.8\%}                                                                           & 93.1±0.8\%                                                                                        & 92.1±0.6\%                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{MobileNet v2}               & Transfer learning                               & 88.5±3.0\%                                                                                    & 78.2±2.0\%                                                                                         & 75.6±1.4\%                                                                                   & \multirow{2}{*}{3.5 million}                                                                \\ 
                                            & Fine Tuning                                     & 98.9±0.5\%                                                                                    & \textbf{97.3±0.4\%}                                                                                & \textbf{95.7±0.3\%}                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{ResNet-50}                  & Transfer learning                               & 39.2±6.0\%                                                                                    & 24.1±4.5\%                                                                                         & 20.2±5.0\%                                                                                   & \multirow{2}{*}{25 million}                                                                 \\ 
                                            & Fine Tuning                                     & 86.7±1.5\%                                                                                    & 78.5±3.0\%                                                                                         & 72.1±2.5\%                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{DenseNet-121}               & Transfer learning                               & 79.2±2.8\%                                                                                    & 70.1±5.5\%                                                                                         & 63.0±4.5\%                                                                                   & \multirow{2}{*}{8 million}                                                                  \\ 
                                            & Fine Tuning                                     & 97.3±0.2\%                                                                                    & 94.7±0.5\%                                                                                         & 93.8±1.0\%                                                                                   &                                                                                             \\ \hline
\multirow{2}{*}{NasNet Mobile}              & Transfer learning                               & 80.1±1.0\%                                                                                    & 76.3±2.0\%                                                                                         & 72.4±1.8\%                                                                                   & \multirow{2}{*}{4.3 million}                                                                \\ 
                                            & Fine Tuning                                     & 95.4±0.5\%                                                                                    & 91.2±1.0\%                                                                                         & 87.8±1.2\%                                                                                   &                                                                                             \\ \hline
Proposed CNN                                & Train from scratch                              & 98.7±0.3\%                                                                                    & 96.5±0.3\%                                                                                         & 95.3±0.5\%                                                                          & \textbf{2.4 million}                                                                        \\ \hline
\end{tabular}
\end{table}


The result reported at table 3 indicates that for all of the CNN models fine-tuning produced a better result than the transfer learning method. MobileNet v2 architecture performed best among the state-of-the-art CNN architectures with small parameter size. The other deep CNN architectures with big parameters size mostly failed to identify the test and validation rice plant images correctly. In the transfer learning method, we noticed that most of the CNN architectures showed overfitting problem. Even during the fine-tuning method, Inception v3 and ResNet-50 model showed overfitting characteristics. The architectures performed better in training data but failed in validation and test data. Figure 5 shows the bar chart of training, validation and test accuracy for all the state-of-the-art CNN architectures. It is visible that fine-tuning improved the overall classification accuracy for all of the CNN architectures.

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.19]{Accuracy graph.jpg}}
\vspace{.1em}
\caption{State-of-the-art CNN model performance graph}
\label{fig}
\end{figure}



\subsection{Proposed CNN model Performance}
Our proposed model was trained from scratch and the weights were updated accordingly. By hyperparameter tuning, we have achieved a validation and testing accuracy of 96.5\% and 95.3\% respectively. Considering the small parameter size, our model performed significantly well in detecting various rice plant diseases. Figure 6 demonstrates the best loss and accuracy curve achieved with Adam optimizer with a 0.001 learning rate for our proposed model.

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.61]{curve.jpg}}
\vspace{.1em}
\caption{Performance curve of the proposed CNN model}
\label{fig}
\end{figure}

From the loss and accuracy curve, it is evident that our proposed model slowly converged to the rice disease dataset without showing any overfitting characteristics. We used different optimizers for updating the weights. 
Table 4 shows the performance of using different optimizers with variable learning rates on our rice plant disease dataset. 


\begin{table}[htbp]
\caption{Optimizers Performance With Different Learning Rate On Rice Plant Dataset}
\centering
\fontsize{9pt}{10pt}\selectfont
\begin{tabular}{ccc} 
\hline
\textbf{Optimizer}                  & \textbf{Learning Rate} & \textbf{Mean Validation Accuracy}  \\ 
\hline
\multirow{3}{*}{Adam}      & 0.01          & 95.4±0.4\%               \\ 

                           & 0.001         & \textbf{96.5±0.3\%}               \\ 

                           & 0.0001        & 95.1±0.4\%               \\ 
\hline
\multirow{3}{*}{RmsProp}   & 0.01          & 94.1±0.3\%               \\ 

                           & 0.001         & 95.01±0.3\%              \\ 

                           & 0.0001        & 94.4±0.4\%               \\ 
\hline
\multirow{3}{*}{HingeLoss} & 0.01          & 78.2±1.8\%               \\ 

                           & 0.001         & 82.7±1.2\%               \\ 

                           & 0.0001        & 73.5±1.5\%               \\ 
\hline
\multirow{3}{*}{SGD}       & 0.01          & 89.2±1.0\%               \\ 

                           & 0.001         & 88.5±1.5\%               \\ 

                           & 0.0001        & 82.1±1.5\%               \\
\hline
\end{tabular}
\end{table}

We used different optimizers such as Adam, Rmsprop, Hingeloss, SGD along with variable learning rates on our proposed CNN model. Among the optimizers, Adaptive Moment Estimation (Adam) [31] outperformed all the other optimizers in terms of accuracy. Rmsprop optimizer performed similarly to Adam optimizer. 

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.77]{learning rate.jpg}}
\vspace{.1em}
\caption{Adam optimizer with different learning rate}
\label{fig}
\end{figure}

Stochastic Gradient Descent (SGD) and the SVM optimizer Hingeloss optimizer didn’t perform as well as Adam or Rmsprop. Figure 7 shows the accuracy curve of the Adam optimizer with different learning rates on our rice disease dataset. From figure 7 we can observe that the learning rate of 0.01 converged very quickly to our rice disease dataset. On the other hand, the learning rate of 0.001 converged slowly to our dataset but achieved overall maximum accuracy in the validation dataset. Hence, the optimal learning rate for our model is 0.001. The normalized confusion matrix for the testing data of our proposed model is shown in figure 8. 

\begin{figure}[htbp]
\centerline{\includegraphics[scale=0.30]{CMatrix.jpg}}
\vspace{.1em}
\caption{Normalized confusion matrix of the proposed model on test dataset. }
\label{fig}
\end{figure}

Our proposed model achieved a mean testing accuracy of 95.3\%. If we analyze the normalized confusion matrix, we can observe that apart from the brown spot disease all of the other disease classes have high detection accuracy which clearly indicates that our proposed model can identify the diseases correctly. Considering a small parameter size, it is evident that our proposed model performed significantly well in detecting rice plant diseases.

\subsection{Discussions}
From our experimental analysis we have acquired some key findings. The findings are reported below.
\begin{itemize}

\item Using the transfer learning method with pre-trained imagenet weights on state-of-the-art CNN architectures cannot produce good results on our rice disease dataset. Fine tuning provides better accuracy than transfer learning in all eight state-of-the-art CNN architectures.


\item Lightweight CNN architectures with small parameters size such as (MobileNet v2, DenseNet-121, NasNet Mobile) converge quickly to our dataset.

\item The deep CNN architectures with pre-trained imagenet weights cannot converge quickly to our rice plant disease dataset and mostly shows overfitting characteristics.

\item Categorical cross-entropy with Adam optimizer performs better in updating the weights for the dense layer on proposed model. However, different learning rate provides almost similar performance which indicates that learning rate has very limited effect on updating the weights.


\item A learning rate of 0.001 and momentum of 0.9 performs better and produces better accuracy in classifying rice plant diseases.

\end{itemize}



\section{Conclusion}
\label{}
In this paper, a depthwise separable convolution based neural network model has been proposed that can effectively identify 12 kinds of rice plant diseases along with healthy rice plants. Initially, the sample images were collected from different regions of Bangladesh and during the pre-processing method Contrast Limited Adaptive Histogram Equalization algorithm (CLAHE) has been implemented to enhance the contrast of the images and to reduce the noise. Along with histogram equalization different image augmentation method has been performed by which the sample images of the dataset are increased to 16770 images. Besides our proposed model, 8 different state-of-the-art CNN architectures have been used on the rice disease dataset. MobileNet v2 architecture performed best among all state-of-the-art CNN architectures with a validation and testing accuracy of 97.3\% and 95.7\% respectively. The proposed CNN model has been evaluated with different loss functions along with variable learning rates. Adam optimizer with a learning rate of 0.001 provided the best validation and test accuracy of 96.5\% and 95.3\% respectively for our proposed CNN model. It is evident that our proposed model performed significantly well in detecting various rice plant diseases accurately. Considering a small parameter size of 2.4 million, the proposed model is a substantial improvement over the traditional convolutional neural network architectures in rice plant disease detection.

\section*{Acknowledgement}
\label{}
The Authors would like to thank the Information and Communication Technology (ICT) division of Bangladesh for funding this research.

%% The Appendices part is started with the command \appendix;
%% appendix sections are then done as normal sections
%% \appendix

%% \section{}
%% \label{}

%% References
%%
%% Following citation commands can be used in the body text:
%% Usage of \cite is as follows:
%%   \cite{key}         ==>>  [#]
%%   \cite[chap. 2]{key} ==>> [#, chap. 2]
%%

%% References with BibTeX database:

\bibliographystyle{IEEEtran}
%\bibliography{<your-bib-database>}

%% Authors are advised to use a BibTeX database file for their reference list.
%% The provided style IEEEtran.bst formats references is generally used.

%% For references without a BibTeX database:

\begin{thebibliography}{99}
\itemsep 0pt

%%The main references are international journals and proceedings. All references should be to the most pertinent, up-to-date sources and the minimum of references are 25. References are written in IEEE style. Please use a consistent format for references – see examples below:

%% \bibitem must have the following form:
%%   \bibitem{key}...
%%

 \bibitem{OU} S.H. Ou, \textsl{Rice Diseases,} 2nd ed. Commonwealth Mycological Institute, ISBN 0 85 198 545 9, Ferry Lane, Kew, Surrey, England, 1985.

 \bibitem{Lu}	Y. Y. Lu, \textsl{et al.,} "Identification of Rice Diseases using Deep Convolutional Neural Networks," \textsl{Neurocomputing}, vol. 267, pp. 378-384, 2017.

 \bibitem{Liu} C. Liu, \textsl{et al.,} "Study on identification of Rice False Smut based on CNN in natural environment," in \textsl{10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai}, pp. 1-5, 2017.

 \bibitem{Jagan} K.J. Mohan \textsl{et al.,} "Detection and Recognition of Diseases from Paddy Plant Leaf Images," \textsl{International Journal of Computer Applications}, vol. 144, pp. 34-41, 2016.

\bibitem{Ihsan}	I. Ihsan, \textsl{et al.,} "Identification of Bacterial Leaf Blight and Brown Spot Disease in Rice Plants With Image Processing Approach," \textsl{Jurnal Ilmiah Teknik Elektro Komputer dan Informatika,} vol. 5, pp. 59-67, 2020.

\bibitem{Ramesh} S. Ramesh and D. Vydeki, "Recognition and Classification of Paddy Leaf Diseases Using Optimized Deep Neural Network with Jaya Algorithm," \textsl{Information Processing in Agriculture,} vol. 7, no. 2, pp. 249-260, 2019.

\bibitem{Krishnamoorthy} N. Krishnamoorthy,\textsl{et al.,} "Rice leaf diseases prediction using deep neural networks with transfer learning," \textsl{Environmental Research,} vol. 198, 2021.

\bibitem{Shrivastava} V. K. Shrivastava, \textsl{et al.,} "Rice Plant Disease Classification Using Transfer Learning of deep CNN," \textsl{Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,} vol.  XLII-3, pp. 631-635, 2019.

\bibitem{Prottasha} M. S. I. Prottasha, \textsl{et al.,} "A Lightweight CNN Architecture to Identify Various Rice Plant Diseases in Bangladesh," in \textsl{2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD),} pp. 316-320, 2021.


\bibitem{tomato} Y. Chai and X. D. Wang,  "Recognition of greenhouse tomato disease based on image processing technology," \textsl{Tech. Autom. Appl}, vol. 9, pp. 83-89, 2013.
 
\bibitem{wheat} K. Khairnar and R. Dagade,  "Disease detection and diagnosis on plant using image processing - a review," \textsl{International Journal of Computer Applications}, vol. 108, pp. 36-39, 2014.
  
\bibitem{cotton} Z. Shicha, \textsl{et al.,} "Morphological feature extraction for cotton disease recognition by machine vision," \textsl{Microcomputer information,} vol. 23, pp. 290-292, 2007.
   
\bibitem{apple} L. W. Huang and D. J. He,  "Ripe Fuji Apple Detection Model Analysis in Natural Tree Canopy," \textsl{Indonesian Journal of Electrical Engineering,} vol. 10, pp. 1771-1778, 2012.

\bibitem{peach} S. Yadav, \textsl{et al.,} "Identification of disease using deep learning and evaluation of bacteriosis in peach leaf," \textsl{Ecological Informatics,} vol. 61, 2021

\bibitem{Zhou} G. Zhou, \textsl{et al.,} "Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion," \textsl{IEEE Access,} vol. 7, pp. 143190-143206, 2019.

 \bibitem{sethy} P.K. Sethy, \textsl{et al.,} "Rice false smut detection based on faster R-CNN," \textsl{Indonesian Journal of Electrical Engineering and Computer Science}, vol. 19, pp. 1590-1595, 2020.

 \bibitem{Bari} B.S. Bari, \textsl{et al.,} "A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework," \textsl{PeerJ Computer Science}, vol. 7, 2021.

 \bibitem{Rahman} R. Rahman, \textsl{et al.,} "Identification and recognition of rice diseases and pests using convolutional neural networks," \textsl{Biosystems Engineering}, vol. 194, pp. 112-120, 2020.

 \bibitem{Yoshiya} Y. Tagami, "Epidemiology of the Bacterial Leaf Blight of Rice," \textsl{Japanese Journal of Phytopathology}, vol. 31, pp. 145-151, 1965.

 \bibitem{Ram} R. Singh, \textsl{et al.,} Brown spot of rice: an overview," \textsl{Indian Phytopath}, vol. 12, pp. 201-215, 2014.
 
 \bibitem{Hatiro} H. Ikegami, "Studies on the false smut of rice," \textsl{Japanese Journal of Phytopathology}, vol. 27, pp. 16-23, 1962.

\bibitem{Roumenr} E. C. Roumen, and W. S. De Boef, "Latent period to leaf blast in rice and its importance as a component of partial resistance," \textsl{Euphytica,} vol. 69, pp. 185-190, 1993.

\bibitem{Xue} J. Xue, \textsl{et al.,} "Transcriptome analysis of the brown planthopper Nilaparvata lugens," \textsl{PloS one,} vol. 5, no. 12, 2010.

\bibitem{Pathak} M.D. Pathak and  Z.R. Khan, \textsl{Insect pests of rice,}  Los Banos, Manila, International Rice Research Institute, 1994.

\bibitem{Pizer}	S. M. Pizer, \textsl{et al.,} "Adaptive Histogram Equalization and Its Variations," \textsl{Computer Vision, Graphics, and Image Processing,} vol. 39, pp. 355-368, 1987.

\bibitem{VGG} K. Simonyan, and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," \textsl{CoRR,} vol. abs/1409.1556 , 2015.

\bibitem{Inception}	C. Szegedy, \textsl{et al.,} "Rethinking the Inception Architecture for Computer Vision," in \textsl{2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV}, pp. 2818-2826, 2016.

 \bibitem{resnet} K. He, \textsl{et al.,} "Deep Residual Learning for Image Recognition," in \textsl{2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV}, pp. 770-778, 2016.

 \bibitem{Xception} F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in \textsl{2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI},  pp. 1800-1807, 2017.
 
 \bibitem{Mobilenet} M. Sandler, \textsl{et al.,} "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in \textsl{2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City},  pp. 4510-4520, 2018.

 \bibitem{Adam} D. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," in \textsl{3rd International Conference for Learning Representations, San Diego, 2015},  pp. 1-15, 2015.


 \end{thebibliography}

\section*{BIOGRAPHIES OF AUTHORS} 
%% make sure to add \vspace{-.7em} before \begin{biography}
\vspace{-.7em} 

\begin{biography}[{\includegraphics[width=2.5cm,height=3.5cm,clip,keepaspectratio]{pro.jpg}}]
\textbf{Md. Sazzadul Islam Prottasha} %% Affiliation and educational background.
is a graduate student at Bangladesh University of Professionals. He obtained Bachelor Degree in Information and Communication Engineering from Bangladesh University of Professionals in 2018. Currently he is working in multiple project based on image processing and computer vision.

%% Research interests, teaching, professional experiences, etc.
His  research  interests  include  Machine Learning, Computer  Vision, Image Processing, Big Data Analytics,  Artificial  Intelligence,  and IOT.





\end{biography}

\begin{biography}[{\includegraphics[width=2.5cm,height=3.5cm,clip,keepaspectratio]{salim reza.jpg}}]
\textbf{S M Salim Reza}
is an Assistant Professor of the Department of ICT, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh. He is a Professional Member of IEEE and many other professional  organizations. He is a keen planner and implementer with proficiency in managing academic and administrative activities entailing operational policies/norms, faculty appraisal/ computer science training. He has more than 50 research papers in ISI/ Scopus Indexed journal and many reputed International conferences. 



\end{biography}


\end{document}

%%
%% End of file `iaesarticle.tex'. 