An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

,; Senthilkumar, C.; Kamarasan, M.; ,

doi:10.35940/ijeat.b4066.129219

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…The network's hidden layers will then extract essential features from certain portions of the image, such as sharpness, texture, and picture shadowing. In order to identify disease, the deepest layer will ultimately collect high‐level information from the retinal image, such as shape 23 . The following subsections provide an explanation of the procedures that make up the Inception‐ResNet V2 based model.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Jain,

Bhatnagar,

Rao

et al. 2023

Computational Intelligence

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Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre‐processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception‐ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.

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Section: Proposed Methodologymentioning

confidence: 99%

“…In order to identify disease, the deepest layer will ultimately collect high-level information from the retinal image, such as shape. 23 The following subsections provide an explanation of the procedures that make up the Inception-ResNet V2 based model.…”

Section: Cnn (Inception-resnet V2)mentioning

confidence: 99%

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Jain,

Bhatnagar,

Rao

et al. 2023

Computational Intelligence

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“…[ 27,59,72,76,78] Black spot These are small, round, and dangerous spots with a diameter of 0.12 to 0.…”

Section: Anthracnosementioning

confidence: 99%

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Dhiman¹,

Kaur

Balasaraswathi

et al. 2023

Sustainability

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Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.

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“…Traditional approaches (e.g. classification, feature extraction, pre‐processing, and segmentation) were employed to identify and classify citrus diseases 14 . Inception ResNetc (https://keras.io/api/applications/inceptionresnetv2/), which extracts features, and a random forest classifier were used to attain 98.91% accuracy.…”

Section: Related Literaturementioning

confidence: 99%

Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

Cited by 8 publications

References 11 publications

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Retina disease prediction using modified convolutional neural network based on Inception‐ResNet model with support vector machine classifier

Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Using a novel convolutional neural network for plant pests detection and disease classification

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