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

Prottasha, Md. Sazzadul Islam; Reza, Sayed Mohsin Salim

doi:10.11591/ijece.v12i4.pp3642-3654

Cited by 19 publications

(16 citation statements)

References 22 publications

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“…The Proposed DNN achieved an accuracy rate of 99.6 percent, with a 1.75 percent increase over other similar projects with hardware implementation of the DNN using the XSG. The error rate implementation using (8,6) scenario was 7e-4 with a difference of 28 percent less than similar projects and using 306 fewer hardware components, which represents 30 percent of the FPGA device space. In conclusion, the proposed DNN design successfully reduced the hardware space utilization on FPGA devices while achieving a higher accuracy rate.…”

Section: Discussionmentioning

confidence: 91%

“…The hardware implementation of deep neural network (DNN) must ensure the efficient diagnosis of breast cancer with minimum possible hardware requirement on the field programmable gate array (FPGA) [2]. Xilinx company provides tools to work with programmable hardware using Xilinx system generator (XSG) to implement the DNN [3]- [6]. XSG will convert a block diagram implemented in Simulink into a very hardware description language (VHDL) that can be used to program an FPGA board [3], [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

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Reduced hardware requirements of deep neural network for breast cancer diagnosis

Tabra

Tawfeeq

2022

IJ-AI

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Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.

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Section: Discussionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Reduced hardware requirements of deep neural network for breast cancer diagnosis

Tabra

Tawfeeq

2022

IJ-AI

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“…This work considered 45 articles for the survey. The article [ 9 ] discusses the classification of 12 classes of rice diseases. Depth wise separable CNN approach is used for classification and 95.3% testing accuracy is achieved by the proposed model.…”

Section: Related Workmentioning

confidence: 99%

A novel GCL hybrid classification model for paddy diseases

Lamba

Baliyan

Kukreja

2022

Int. j. inf. tecnol.

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The demand for agricultural products increased exponentially as the global population grew. The rapid development of computer vision-based artificial intelligence and deep learning-related technologies has impacted a wide range of industries, including disease detection and classification. This paper introduces a novel neural network-based hybrid model (GCL). GCL is a dataset-augmentation fusion of long-short term memory (LSTM) and convolutional neural network (CNN) with generative adversarial network (GAN). GAN is used for the augmentation of the dataset, CNN extracts the features and LSTM classifies the various paddy diseases. The GCL model is being investigated to improve the classification model’s accuracy and reliability. The dataset was compiled using secondary resources such as Mendeley, Kaggle, UCI, and GitHub, having images of bacterial blight, leaf smut, and rice blast. The experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy. GCL can further be used for the classification of more diseases of paddy.

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“…In fact, deep neural networks are unable to tackle multidimensional input efficiently such as an image, as the parameters during training will be massive which is impractical [6]. As an alternative, convolutional neural network (CNN) was introduced to interpret image data and perform image classification related tasks in several area such as algriculture [7], [8], medical imaging [9]- [12] and security and surveillance [13]- [16]. It has been extensively used in visual recognition with its great capability in feature extraction and classification with single-object-image [17].…”

Section: Introductionmentioning

confidence: 99%

Multiple face mask wearer detection based on YOLOv3 approach

As’ari

Sufri

2023

IJ-AI

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<div align="left"><a name="_Hlk108683337"></a><span lang="EN-US">The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. </span></div>

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

Cited by 19 publications

References 22 publications

Reduced hardware requirements of deep neural network for breast cancer diagnosis

Reduced hardware requirements of deep neural network for breast cancer diagnosis

A novel GCL hybrid classification model for paddy diseases

Multiple face mask wearer detection based on YOLOv3 approach

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