Application of convolutional neural networks for evaluation of disease severity in tomato plant

Verma, Shradha; Chug, Anuradha; Singh, Amit Prakash

doi:10.1080/09720529.2020.1721890

Cited by 70 publications

(26 citation statements)

References 13 publications

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“…Many clustering algorithms such as clustering based on density, k-means clustering, fuzzy k-means clustering, hierarchical clustering, and so on are available. As our approach deals with segregating numerous pixels, the k-means clustering algorithm was chosen [18]. K-means clustering was chosen because it enables faster clustering of many variables and constructs tighter clusters.…”

Section: K-means Clustering Algorithm For Image Segmentationmentioning

confidence: 99%

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Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Devi¹,

P.²,

Ar.³

et al. 2021

Symmetry

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Plant diseases pose a severe threat to crop yield. This necessitates the rapid identification of diseases affecting various crops using modern technologies. Many researchers have developed solutions to the problem of identifying plant diseases, but it is still considered a critical issue due to the lack of infrastructure in many parts of the world. This paper focuses on detecting and classifying diseases present in the leaf images by adopting a hybrid learning model. The proposed hybrid model uses k-means clustering for detecting the disease area from the leaf and a Convolutional Neural Network (CNN) for classifying the type of disease based on comparison between sampled and testing images. The images of leaves under consideration may be symmetrical or asymmetrical in shape. In the proposed methodology, the images of various leaves from diseased plants were first pre-processed to filter out the noise present to get an enhanced image. This improved image enabled detection of minute disease-affected regions. The infected areas were then segmented using k-means clustering algorithm that locates only the infected (diseased) areas by masking the leaves’ green (healthy) regions. The grey level co-occurrence matrix (GLCM) methodology was used to fetch the necessary features from the affected portions. Since the number of fetched features was insufficient, more synthesized features were included, which were then given as input to CNN for training. Finally, the proposed hybrid model was trained and tested using the leaf disease dataset available in the UCI machine learning repository to examine the characteristics between trained and tested images. The hybrid model proposed in this paper can detect and classify different types of diseases affecting different plants with a mean classification accuracy of 92.6%. To illustrate the efficiency of the proposed hybrid model, a comparison was made against the following classification approaches viz., support vector machine, extreme learning machine-based classification, and CNN. The proposed hybrid model was found to be more effective than the other three.

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Section: K-means Clustering Algorithm For Image Segmentationmentioning

confidence: 99%

“…The architecture of CNN [18] used to categorize the disease and predict the severity is shown in the Figure 5. CNN is the most efficient and accurate classifier, with several advantages over other classifiers in terms of memory and training time.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Devi¹,

P.²,

Ar.³

et al. 2021

Symmetry

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show abstract

“…In this CNN Model, 1400 leaf images had been trained in suggested 8 layers and 98.4% of accuracy was obtained. Verma et al [14] developed a CNN architectures-based model which works in two different modes for extracting the features and then the extracted feature set was trained in a multiclass SVM to get the final output. The author compared the accuracy of the proposed AlexNet model with other two networks SqueezeNet and Inception V3.…”

Section: Introductionmentioning

confidence: 99%

Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease

Mahakud

Pattanayak

Pati

2022

IJEECS

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A deep transfer learning (deep-TL) classification model has been proposed to diagnose tomato leaf disease. The main challenge of inaccurate classification of a convolution neural network (CNN) model was the availability of the small-sized dataset. This model deals with the challenges like availability of small-sized and imbalanced datasets. The proposed Alex support vector machine (SVM) fused hybrid classification (ASFHC) model is based on fully fusion technology that avoids overfitting to classify the type of disease in tomato leaves. The proposed model achieves the best performance in terms of accuracy by data augmentation of the training data. It uses a pre-trained network for feature extraction with the modification of architecture by concatenating two layers FC6 and FC7 (fully connected layer), plus a linear SVM classifier for classification of the disease. The uniqueness of the research is although the dataset is not balanced, the performance of the model has achieved the maximum. Compared with VGG 16 and VGG 19, the proposed model (ASFHC) has been evaluated using different measuring parameters, indicating remarkable computation time for implementation in the internet of things (IoT) domain. The overall accuracy attained by the model is 99.62%.

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“…The results showed that VGG16 had the best effect and the accuracy reached 90.4%. Verma et al 36 used AlexNet, SqueezeNet 37 and Inception V3 network models to evaluate tomato late blight and divide the disease into three stages early, middle and end. But, they only analyzed one disease of crop, which was not extensive and was not suitable for the classification of disease levels.…”

Section: Introductionmentioning

confidence: 99%

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

Zhao

Chen

et al. 2021

Concurrency and Computation

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Summary Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification. The network embeds the improved channel and spatial attention module into the residual block of ResNet. The proposed attention module reduces the redundancy of information between channels and focuses on the most information‐rich regions of the feature map. In this experiment, SEV‐Net achieved an accuracy of 97.59% and 95.37% for multiple and single plant (Tomato) disease severity classification, which was better than existing attentional networks (SE‐Net and CBAM). Moreover, the combination of visualization techniques showed that SEV‐Net was adept at distinguishing small variations between plant diseases, proving the feasibility and effectiveness of the network. Furthermore, we have also designed and developed an Android application for real‐time classification of plant disease severity. The system deploys the SEV‐Net network model, which has higher classification accuracy and faster recognition speed.

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Application of convolutional neural networks for evaluation of disease severity in tomato plant

Cited by 70 publications

References 13 publications

Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Categorizing Diseases from Leaf Images Using a Hybrid Learning Model

Internet of things and multi-class deep feature-fusion based classification of tomato leaf disease

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

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