Fruit tree disease classification system using generative adversarial networks

Kim, Changsu; Lee, Hyesoo; Jung, Hoe-Kyung

doi:10.11591/ijece.v11i3.pp2508-2515

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…For example, several studies have explored classification with deep learning using the RNN for Fruit image classification [2]. and Fruit tree disease classification using GAN [16].…”

Section: Proposed Methodsmentioning

confidence: 99%

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Shallots Classification using CNN

Wathan

2022

ijicom

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Shallots are an essential plant for the commercial and home industries included in the Allium genus. Choosing the type of onion based on the characteristics is very easy for humans to do but not easy for humans to do in the spice industry; therefore, machines will replace human limitations in recognizing the type of shallots in the spice industry. Inspired by the success of research on the classification of shallots using SVM, we propose CNN to tackle the problem of classifying types of shallots based on shape and texture features. This study uses the CNN method's performance to categorize different varieties of shallots based on their shape and texture features. In a shallot classification test, our approach promises higher accuracy and lower loss than standard machine techniques.

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“…For example, several studies have explored classification with deep learning using the RNN for Fruit image classification [2]. and Fruit tree disease classification using GAN [16].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…An article classifies fruit images using the fruit recognition approach based on the Pure Convolutional Neural Network framework (PCNN) [12]. To generate classification accuracy, many studies investigate various deep learning algorithms, together with RNN [2], CNN [ [13], [14], [15]], and GAN [16], to achieve the highest possible results.…”

Section: Introductionmentioning

confidence: 99%

Shallots Classification using CNN

Wathan

2022

ijicom

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“…Moreover, in the context of the internet of things (IoT), GANs enhance security by training adversaries [18]. From literature, GANs find application in agriculture [19]. They contribute to the identification and classification of diseases in various parts of the plants.…”

Section: Generative Adversarial Network For Data Augmentationmentioning

confidence: 99%

Implementing generative adversarial networks for increasing performance of transmission fault classification

Goswami,

Roy,

Kalavala

et al. 2024

IJEECS

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<p>An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach.</p>

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“…With the 'weighted' average parameter, the other metrics are calculated for each label. By having a class imbalance, which occurs when there is a significant difference in the amount of data corresponding to each class [39]; a weighted average makes more sense, where the weights are calculated by the frequency of a certain class, weighting the metric of each class by the number of samples of that class [40].…”

Section: Comparison Of Machine Learning Modelsmentioning

confidence: 99%

Incident forecasting model for motorcycle driving based on IoT and artificial intelligence

Cárdenas-Lancheros

Vera-Parra

2021

IJEECS

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Internet of things (IoT) and artificial intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on the analysis of dynamic behavior while driving. The development of this research began with the detection and storage of data associated with the dynamic acceleration variable of a motorcycle while driving, this with the help of a 3-axis accelerometer sensor generating a dataset, which was processed and analyzed for later be taken by three predictive classification models based on machine learning which were decision trees, K-Nearest neighbors and random forests. The performance of each model was evaluated in the task of better classifying the level of accident risk, concerning the driving style based on certain levels of acceleration. The random forest model showed a slightly better performance compared to that shown by the other two models, with 97.24% accuracy and recall, 97.16% precision and 97.17% F1 score.

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Fruit tree disease classification system using generative adversarial networks

Cited by 8 publications

References 9 publications

Shallots Classification using CNN

Shallots Classification using CNN

Implementing generative adversarial networks for increasing performance of transmission fault classification

Incident forecasting model for motorcycle driving based on IoT and artificial intelligence

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