VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation

Abas, Mohamad Aqib Haqmi; Ismail, Nurlaila; Yassin, Ahmad Ihsan Mohd; Taib, Mohd Nasir

doi:10.14419/ijet.v7i4.11.20781

Cited by 27 publications

(6 citation statements)

References 6 publications

(11 reference statements)

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“…Liu et al [13] used conventional image rotation, brightness adjustment, and principal component analysis methods to expand the image, solve the problem of insufficient images of apple disease, and achieve good recognition results. Abas et al [14] used rotation, translation, scaling, and other methods to enhance the plant image data and used an improved VGG16 network to classify plants, so as to solve the problem of overfitting caused by too few plant samples. This kind of generation method can generate a large amount of data, which solves the problem of insufficient data to a certain extent but still has the following shortcomings: the image expanded by geometric transformation contains less information and is temporary, and a partially expanded image is not conducive to the training of the model, is not applicable to some tasks, and even reduces the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

et al. 2021

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Accurate recognition of tomato diseases is of great significance for agricultural production. Sufficient and insufficient training data of supervised recognition neural network training are symmetry problems. A high precision neural network needs a large number of labeled data, and the difficulty of data sample acquisition is the main challenge to improving the performance of disease recognition. [l.]Moreover, the traditional data augmentation based on geometric transformation can obtain less information, and the generalization is not strong. In order to generate leaves with obvious disease feature and improve the performance of disease recognition, this paper analyzes and solves the problem of insufficient training samples in recognition network training, and proposes a new data augmentation method RAHC_GAN based on GAN, which is used to expand data and identify diseases. First, the proposed hidden variable is used to control the size of the disease area continuously, and the residual attention blocks are used to make the generated adversarial network pay more attention to the disease region in the leaf image, besides, a multi-scale discriminator is used to enrich the detailed texture of the generated image. Then, an expanded data set including original training set images and generated images by RAHC_GAN is established, which is used as the input of four kinds classification networks AlexNet, VGGNet, GoogLeNet and ResNet for performance evaluation. Experimental results show that RAHC_GAN can generate leaves with obvious disease feature, and the generated expanded data set can significantly improve the recognition performance of the classifier. After data augmentation, the recognition effect on the four classifiers is increased by 1.8%, 2.2%, 2.7%, and 0.4% respectively, which are higher than the comparison method. At the same time, the impact of expanded data with different ratio on the recognition performance was evaluated, and the method was extended to apple and grape diseased leaves. The proposed data augmentation method can simulate the distribution of tomato leaf diseases and improve the performance of disease recognition, and it may be extended to solve the problem of insufficient data in other plant research tasks.The tomato leaf data augmented by the traditional data augmentation methods based on geometric transformation usually contain less information, and the generalization is not strong. Therefore, a new data augmentation method, RAHC_GAN, based on generative adversarial networks is proposed in this paper, which is used to expand tomato leaf data and identify diseases. In this method, continuous hidden variables are added at the input of the generator, and the purpose is to continuously control the size of the generated disease area and to supplement the intra class information of the same disease. Additionally, the residual attention block is added to the generator to make it pay more attention to the disease region in the leaf image; a multi-scale discriminator is also used to enrich the detailed texture of the generated image and finally generate leaves with obvious disease features. Then, we use the images generated by RAHC_GAN and the original training images to build an expanded data set, which is used to train four kinds of recognition networks, AlexNet, VGGNet, GoogLeNet, and ResNet, and the performance is evaluated through the test set. Experimental results show that RAHC_GAN can generate leaves with obvious disease features, and the generated expanded data set can significantly improve the recognition performance of the classifier. Furthermore, the results of the apple, grape, and corn data set show that RAHC_GAN can also be used as a method to solve the problem of insufficient data in other plant research tasks.

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Section: Introductionmentioning

confidence: 99%

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

et al. 2021

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“…This section provides an overview of the evaluation metrics used in our experiments. Mainly, the research community used the accuracy, F1 score, precision, and recall to evaluate the model's performance in the test dataset testing process, regarding the classification model, object recognition in machine learning, deep learning, and information retrieval [71].…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

et al. 2023

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Malware has become increasingly prevalent in recent years, endangering people, businesses, and digital assets worldwide. Despite the numerous techniques and methodologies proposed for detecting and neutralizing malicious agents, modern automated malware creation methods continue to produce malware that can evade modern detection techniques. This has increased the need for advanced and accurate malware classification and detection techniques. This paper offers a unique method for classifying malware, using images that use dual attention and convolutional neural networks. Our proposed model has demonstrated exceptional performance in malware classification, achieving the remarkable accuracy of 98.14% on the Malimg benchmark dataset. To further validate its effectiveness, we also evaluated the model’s performance on the big 2015 dataset, where it achieved an even higher accuracy rate of 98.95%, surpassing previous state-of-the-art solutions. Several metrics, including the precision, recall, specificity, and F1 score were used to evaluate accuracy, showing how well our model performed. Additionally, we used class-balancing strategies to increase the accuracy of our model. The results obtained from our experiments indicate that our suggested model is of great interest, and can be applied as a trustworthy method for image-based malware detection, even when compared to more complex solutions. Overall, our research highlights the potential of deep learning frameworks to enhance cyber security measures, and mitigate the risks associated with malware attacks.

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“…VGG19 compresses five convolutional layers before being combined in a multilayer perceptron (MLP). The last layer includes nodes that directly contain the number of classified classes (for some classes) or a sigmoid activation function (for classes that do not more than or equal to two) [16].…”

Section: Figure 2 Vgg19 Architecturementioning

confidence: 99%

Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

Priyangka

Kumara

2021

LKJITI

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Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.

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VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation

Cited by 27 publications

References 6 publications

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

Mitigating the Risks of Malware Attacks with Deep Learning Techniques

Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method

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