An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Senan, Norhalina; Aamir, Muhammad; Ibrahim, Rosziati; Taujuddin, Nik Shahidah Afifi; Muda, W.H.N Wan

doi:10.14569/ijacsa.2020.0110716

Cited by 30 publications

(3 citation statements)

References 27 publications

(36 reference statements)

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“…Also, unnecessary financing and wasting the resources should be reduced so as to have a risk-free production of rice. The advancement of computer vision applications in the field of agriculture provides better opportunity in enhancing the protection of rice crops (Senan et al, 2020). As rice serves as a staple food all over the world, the increase in population leads to greater demand for rice crops.…”

Section: Core Ideasmentioning

confidence: 99%

Detection of rice plant disease using AdaBoostSVM classifier

Kumar

Kannan

2022

Agronomy Journal

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The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

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Section: Core Ideasmentioning

confidence: 99%

Detection of rice plant disease using AdaBoostSVM classifier

Kumar

Kannan

2022

Agronomy Journal

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“…Nowadays, most image processing and computer vision-related problems apply CNN for a better solution [2]. e reason behind the better performance of CNN in the above said task is its ability to work on raw data without having prior knowledge [3]. CNN is a biologically inspired artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Recent Architecture of Convolutional Neural Network

Saleem

Senan

Wahid

et al. 2022

Mathematical Problems in Engineering

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Convolutiona neural network (CNN) is one of the best neural networks for classification, segmentation, natural language processing (NLP), and video processing. The CNN consists of multiple layers or structural parameters. The architecture of CNN can be divided into three sections: convolution layers, pooling layers, and fully connected layers. The application of CNN became most demanding due to its ability to learn features from images automatically, involving massive amount of training data and high computational resources like GPUs. Due to the availability of the above-stated resources, multiple CNN architectures have been reported. This study focuses on the working of convolution, pooling, and the fully connected layers of CNN architecture, origin of architectures, limitation, benefits of reported architectures, and comparative analysis of contemporary architecture concerning the number of parameters, architectural depth, and significant contribution.

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“…In these countries, farmers are facing several issues like shortage of water, natural disaster, and disease on plants. According to data, rice plant disease destroys 10-15 percent of yields in Asia, and the source of this destruction is a fungus and viral disease on rice plant [2]. The example of diseases produced by viral and fungus-like rice blast, bacterial leaf blast and sheath blight.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Saleem¹,

Aamir²,

Ibrahim³

et al. 2022

Computers, Materials &Amp; Continua

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Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology. Nevertheless, the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge. This approach is time-consuming, and high computational resources are mandatory. In this research, we propose a novel method based on Mutant Particle swarm optimization (MUT-PSO) Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification. Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network (MUTPSO-CNN ) can find optimum CNN architecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy, precision/recall, and execution time.

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An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification

Cited by 30 publications

References 27 publications

Detection of rice plant disease using AdaBoostSVM classifier

Detection of rice plant disease using AdaBoostSVM classifier

Comparative Analysis of Recent Architecture of Convolutional Neural Network

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

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