Plant Disease Detection using AI based VGG-16 Model

Alatawi, Anwar Abdullah; Alomani, Shahd Maadi; Alhawiti, Najd Ibrahim; Ayaz, Muhammad

doi:10.14569/ijacsa.2022.0130484

Cited by 23 publications

(12 citation statements)

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“…The VGG-16 network model was proposed by Simonyan 6 The VGG-16 network structure is very regular, there are not so many hyperparameters, and it focuses on building a simple network. It is a few convolutional layers followed by a pooling layer that can compress the image size, which is totally use 3*3 small convolution kernels and 2*2 maximum pooling layers 7 . The figure2 shows the network structure model of VGG-16.…”

Section: The Vgg-16 Netmentioning

confidence: 99%

Research on fine-grained classification of chrysanthemum images based on multi-scale and multi-parallel convolutional neural network

Huang

Liu

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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As a traditional Chinese flower, chrysanthemum has the advantages of long flowering period, many varieties, wide application, and bright colors. However, different varieties of chrysanthemums require different growth environments and have different ornamental and medicinal values. Therefore, the fine-grained and accurate identification of chrysanthemum varieties has important guiding significance for the breeding and cultivation of chrysanthemums. Finegrained image recognition is a more detailed subclassification of coarse-grained large categories. Due to the subtle interclass differences between sub-categories, compared with general image recognition and classification, it is necessary to use a deeper and more complex network structure to extract more complex and higher-order features of images to improve the the accuracy of fine-grained classification of chrysanthemum images. However, the complexity of the network structure will lead to a large number of parameters of the convolutional neural network model, long training time and weak generalization ability. In order to solve the above problems, this study proposes an improved multi-scale, multi-parallel convolutional neural network, VGG-Inception, based on the traditional VGG-16 convolutional neural network. This model increases the parallel network structure and widens the network while ensuring the depth of the network. VGG-Inception uses the global pooling layer to replace the full connection. While ensuring the accuracy of model classification, the number of model parameters is reduced to 9% of the original VGG-16. At the same time, the auxiliary classifier is used to conduct the gradient forward, avoid the gradient disappearance, and improve the generalization ability of the model.

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Section: The Vgg-16 Netmentioning

confidence: 99%

Research on fine-grained classification of chrysanthemum images based on multi-scale and multi-parallel convolutional neural network

Huang

Liu

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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“…They introduced the deep block attention to improve the feature extraction capability of their backbone architecture (i.e., VGG-16). Alatawi et al [32] also utilized a VGG-16 [34] backbone architecture for plant disease classification. They utilized SoftMax and ReLu activation functions along with "Sparse Categorical Cross Entropy Loss" for CNN model training.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 7 corresponds to the combined confusion matrices for all 38 classes on unseen 12,784 test images. Several other CNN-based classifiers, including DenseNet-201 [21,22], DenseNet-121 [22,25,29,33], ResNet-50 [21,30,33], and VGG-16 [26,32,33] were also trained on the same samples. The confusion matrix of each model was generated and combined, and their corresponding true positive rates were compared with the proposed model; the comparison is shown in Figure 8 We calculate the precision, recall, and F1 score of the proposed PDD-Net, as shown in Table 3, for all 38 classes of the PlantVillage benchmark dataset.…”

Section: Plantvillagementioning

confidence: 99%

“…The overall performance was evaluated by taking the average of all classes. We compared the overall performance of PDD-Net with several CNN-based classifiers used for plant disease classification, including DenseNet-201 [21,22], DenseNet-121 [22,25,29,33], ResNet-50 [21,30,33], and VGG-16 [26,32,33].…”

Section: Plantvillagementioning

confidence: 99%

“…Recently, with the advent of domain-specific architectures [18] (i.e., GPU, TPU, etc. ), deep learning (DL)-based classifiers, particularly CNN, are becoming increasingly popular [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A CNN-based classifier (i.e., VGG-Net [34], ImageNet [35], Inception-Net [36], DenseNet-121 [37], MobileNet [38], ResNet50 [39], etc.)…”

Section: Introductionmentioning

confidence: 99%

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PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Alghamdi

Turki

2023

Agriculture

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Overlooked diseases in agriculture severely impact crop growth, which results in significant losses for farmers. Unfortunately, manual field visits for plant disease diagnosis (PDD) are costly and time consuming. Although various methods of PDD have been proposed, many challenges have yet to be investigated, such as early stage leaf disease diagnosis, class variations in diseases, cluttered backgrounds, and computational complexity of the diagnosis system. In this paper, we propose a Convolutional Neural Network (CNN)-based PDD framework (i.e., PDD-Net), which employs data augmentation techniques and incorporates multilevel and multiscale features to create a class and scale-invariant architecture. The Flatten-T Swish (FTS) activation function is utilized to prevent gradient vanishing and exploding problems, while the focal loss function is used to mitigate the impact of class imbalance during PDD-Net training. The PDD-Net method outperforms baseline models, achieving an average precision of 92.06%, average recall of 92.71%, average F1 score of 92.36%, and accuracy of 93.79% on the PlantVillage dataset. It also achieves an average precision of 86.41%, average recall of 85.77%, average F1 score of 86.02%, and accuracy of 86.98% on the cassava leaf disease dataset. These results demonstrate the efficiency and robustness of PDD-Net in plant disease diagnosis.

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Machine learning for pest detection and infestation prediction: A comprehensive review

Mittal,

Gupta,

Aamash

et al. 2024

WIREs Data Min & Knowl

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Pests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction. The paper investigates and presents background information on the necessity of pest control as well as the difficulty in recognizing pests and forecasting. Several strategies, including approaches to data collection, modeling, and assessment of models, are reviewed in the research described. The authors examine various pest detection methods involving the utilization of convolutional neural networks and several object detection architectures categorized broadly into one‐stage and two‐stage object detection algorithms. Methods for predicting pest infestations that involve regression, classification, and time series forecasting are also thoroughly investigated. The challenges of recognizing pests and predicting infestations are underlined, as are issues with data quality, feature selection, and model interpretability. The report also indicates the limitations to pest detection and infestation prediction as well as intriguing topics for further research on the same. The findings of the literature research demonstrate how Artificial Intelligence, Computer Vision, and the Internet of Things have been applied for Pest Detection and Infestation Prediction. The research serves as a base for surveying and summarizing the approaches utilized for the task of pest detection (an object detection problem) and pest infestation prediction (a forecasting problem) and its findings and recommendations serve as a platform for future study and the development of effective pest management solutions.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction

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Plant Disease Detection using AI based VGG-16 Model

Cited by 23 publications

References 0 publications

Research on fine-grained classification of chrysanthemum images based on multi-scale and multi-parallel convolutional neural network

Research on fine-grained classification of chrysanthemum images based on multi-scale and multi-parallel convolutional neural network

PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features

Machine learning for pest detection and infestation prediction: A comprehensive review

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