An effective feature extraction method for rice leaf disease classification

Azim, Muhammad Anwarul; Islam, Mohammad Khairul; Rahman, Md. Shahidur; Jahan, Farah

doi:10.12928/telkomnika.v19i2.16488

Cited by 79 publications

(32 citation statements)

References 19 publications

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“…The boundary of the disease spots is obvious, and the disease spots are not convex. In the initial stage of rice infected with brown spot disease, it is a small brown water-soaked spot, and then it expands into a spindle shaped or irregular reddish-brown stripe, with yellow halo at the edge, grayish brown at the center of the disease spot, and the disease spot often melts into a large stripe, making the leaves locally gray and sterile ( Azim et al., 2021 ). Rice sheath blight can occur from seedling stage to Panicle stage.…”

Section: Resultsmentioning

confidence: 99%

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Lü

Zhang²,

Zeng³

et al. 2022

Front. Plant Sci.

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In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

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Section: Resultsmentioning

confidence: 99%

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Lü

Zhang²,

Zeng³

et al. 2022

Front. Plant Sci.

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“…By removing the background, segmenting the disease area, extracting color, shape, and texture features, they used eXtreme gradient boosting (XGBoost) to enhance the recognition performance. e result showed that the accuracy of 86.58% was achieved [18].…”

Section: Related Studiesmentioning

confidence: 95%

A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Jin

Zhong

et al. 2021

Complexity

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Fruit tree diseases have a great influence on agricultural production. Artificial intelligence technologies have been used to help fruit growers identify fruit tree diseases in a timely and accurate way. In this study, a dataset of 10,000 images of pear black spot, pear rust, apple mosaic, and apple rust was used to develop the diagnosis model. To achieve better performance, we developed three kinds of ensemble learning classifiers and two kinds of deep learning classifiers, validated and tested these five models, and found that the stacking ensemble learning classifier outperformed the other classifiers with the accuracy of 98.05% on the validation dataset and 97.34% on the test dataset, which hinted that, with the small- and middle-sized dataset, stacking ensemble learning classifiers may be used as cost-effective alternatives to deep learning models under performance and cost constraints.

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“…The In earlier many authors were conducted various studies and implementation using a different dataset [10][11][12][13][14][15][16]. The accuracy was not achieved maximum percentage.…”

Section: Literature Survey and Comparison Studymentioning

confidence: 99%

Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification

Karunanithi¹,

Singh²,

Kannapiran³

2022

RIA

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In the Asian continent rice cultivation process provide staple food for livelihood. A current research work in the agriculture area involves recognizing and classifying plants diseases based on live images. Farmer can traditionally do the cultivation process, hence here the identification of the disease was by manual (visual appearance) or send the sample data set to the nearest laboratory. In our proposed method we will provide accurate and early detection of various diseases in Oryza sativa (rice) plants, that can help the farmers in applying suitable treatment on the rice plants and improve productivity. We are using optimized deep learning models such as the ResNet-152, CoAtNet for classification and identify the diseases. We have captured healthy and unhealthy images from Villupuram district, Tamil Nadu, India. The total amount of captured images was 3071 from our farmer's field with proper sunlight. It was highly efficient and detects the diseases or recognizes the diseases from the captured image with different categories (Bacterial Leaf Blight, Leaf Blast, Brown Spot, and Tungro / Leaf smut). The experimental results show according to the proposed method CoAtNet, was achieved for overall achieved accuracy of 96.56%.

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An effective feature extraction method for rice leaf disease classification

Cited by 79 publications

References 19 publications

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

A Fruit Tree Disease Diagnosis Model Based on Stacking Ensemble Learning

Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification

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