A novel approach for rice plant diseases classification with deep convolutional neural network

Upadhyay, Santosh Kumar; Kumar, Avadhesh

doi:10.1007/s41870-021-00817-5

Cited by 124 publications

(44 citation statements)

References 26 publications

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“…As shown in Table 4, the approach introduces in this article performs better than other compared methods. Only model [34] outperforms the proposed method with very less margin (0.5% accuracy), but it is not able to classify the early stage and developed stage of the disease as the proposed model does. The proposed method can easily handle large data sets and is able to extract the features automatically.…”

Section: Comparison With the Existing Workmentioning

confidence: 87%

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Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Upadhyay¹,

Kumar²

2021

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India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

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Section: Comparison With the Existing Workmentioning

confidence: 87%

“…To provide an efficient and effective rice plant disease recognition and classification system Upadhyay and Kumar [34] proposed a novel approach to detect image-based disease in paddy crops using CNN architecture. The authors have presented two experiments, one without segmentation and the other with segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Upadhyay¹,

Kumar²

2021

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“…The size of the dataset is 2370 images. In this study [ 16 ], three rice diseases, leaf smut, brown spot, and bacterial blight are classified using the convolutional neural. The model is trained with 4000 images and gives 99.7% accuracy [ 17 ].…”

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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“…It was shown that DCNNs are effective in foretelling nutritional deficiencies in rice after further testing the performance of the models with a support vector machine and a histogram of oriented gradient with SVM. For the automated identification of various paddy plant maladies, such as LS, BLB, and BS, Upadhyay et al [18] presented CNN model. Initially, all the images were resized and the background noises were removed using Otsu's model and cropped.…”

Section: Related Workmentioning

confidence: 99%

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

Goluguri¹,

Devi²,

CH³

2022

INDJCSE

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In the field of agriculture, identifying and classifying Oryza Sativa diseases is always a hot research topic. Almost in all the states of India, Oryza Sativa (rice) is cultivated as a necessary food crop. However, rice plants are highly caused due to diseases that drastically affect the agricultural sectors. Researchers are searching for solutions for reliable and exact detection approaches for crop leaf diseases. A Oryza Sativa plant leaf disease classification system that is modelled relying on the concept of deep learning-driven Least Square Support Vector Machine is investigated in the present study. This work focus on the Oryza Sativa plant leaf diseases like Brown Leaf spot(BS), Bacterial leaf blight (BLB), Leaf smut(LS), Tungro(LT), and Leaf Blast(LB). To create a contrast-enhanced image dataset, the input Oryza Sativa plant disease image dataset is resized as well as histogram equalization and the Kuwahara filter are applied. The diseased part is segmented using spatial fuzzy clustering (SFC). Then, a multi-model LeNet5 is used in extracting features, and the least square support vector machine(LSSVM) is employed to classify the Oryza Sativa plant leaf diseases images. Further, to improve the accuracy of the disease classification Weighted Sparrow Search Optimization (WSSO) is utilized. The proposed multi-model LSSVM-WSSO outperformed the existing models on the two benchmark datasets when evaluated on the terms of accuracy, precision, recall, and F1-measure values.

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A novel approach for rice plant diseases classification with deep convolutional neural network

Cited by 124 publications

References 26 publications

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

A novel GCL hybrid classification model for paddy diseases

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

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