Plant Classification Based on Leaf Edges and Leaf Morphological Veins Using Wavelet Convolutional Neural Network

Dewi, Wulan; Utomo, Wiranto Herry

doi:10.35784/acs-2021-08

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Neural networks (NN) are computing systems inspired by the biological neural networks (Kohonen, 1982), which learn to solve tasks by considering examples without being programmed with any specific rules. The neural networks were applied to solve the variety of classification tasks in (Alyamani & Yasniy, 2020;Dewi & Utomo, 2021;Sridharan et al, 2021). The training of neural networks can be achieved in several ways, using, for instance, the approach called particle swarm optimization that was employed in (Al-Awad, Abboud & Al-Rawi, 2021).…”

Section: Methodsmentioning

confidence: 99%

Keystroke Dynamics Analysis Using Machine Learning Methods

SHABLIY

Lupenko²,

Lutsyk³

et al. 2021

acs

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The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.

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Section: Methodsmentioning

confidence: 99%

Keystroke Dynamics Analysis Using Machine Learning Methods

SHABLIY

Lupenko²,

Lutsyk³

et al. 2021

acs

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“…Their findings showed that their method performed better than cutting-edge techniques, with an accuracy of 97.58% on the set of images for rice pests and diseases (Shao et al, 2021) proposed a method that merges the LC-FCN model based on pretrained models with the watershed algorithm for dense rice image recognition with an accuracy of 89.88% DAWI and Wulan propose research doing a method using wavelets to denoise the images of the data set and then performs the classification of these images with convolution method. The results obtained using the wavelet method and convolutional neural network gave an accuracy of 97% (Dewi and Utomo, 2021;Yu et al, 2019) and propose a new approach for the detection of apple leaf diseases using deep learning considering regions of interest. They designed two sub-networks in the first step.…”

Section: Introductionmentioning

confidence: 97%

Experimental Evaluation of Coffee Leaf Disease Classification and Recognition Based on Machine Learning and Deep Learning Algorithms

Ayikpa¹,

Diarra²,

Gouton³

et al. 2022

Journal of Computer Science

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Coffee plant diseases constitute a significant danger to world coffee production, and the greatest challenge is to detect these diseases as early as possible to save the crop. Traditional methods are most often based on visual observations, often with errors in diagnosing diseases. Machine Learning has become a tool that presents itself as an alternative for automatically identifying plant diseases. Our study is to implement a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, so we set up a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN. The results of the classical machine learning methods ranged from 81.03 to 100% and the best performance was obtained with SVM and Random Forest; while the deep learning. In comparison, these provided results between 97.37 and 100% with our CNN custom obtaining receiving accuracy with the lowest loss of 0.013%. Accuracy, precision score, recall, and MCC were employed as performance indicators to support this performance.

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“…Lecturers are also required to have the ability to identify plants to assist lecturers in providing students with an understanding of plant taxonomy. Of course, this ability must be adapted to developments in the digital era (Dewi & Utomo, 2021;Kala & Viriri, 2018). The development of science and technology in the field of education is a challenge to create learning media that can assist the learning process to improve the quality of education.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

Lismaya,

Nurlaelah,

Handayani

2024

IJLI

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This study aims to produce learning media in the form of a Leafcapture Identification Application Based on Augmented Reality (AR) in plant morphology courses with accuracy, user interface, and good content. The research method used in this study is Research and Development (RD) with the The ADDIE approach involves a systematic process for designing learning media. In the Analyze stage, the focus is on identifying the need for new learning media, specifically the Leafcapture Identification Application Based on AR, and assessing its feasibility and requirements. In the Design stage, activities include application design, interface design, 3D image creation, and marker design. The Development stage involves gathering relevant content, such as plant leaf images and names, and setting up the application interface. During Implementation, the developed media is used and evaluated by experts and users to assess its impact on learning quality. Media experts validated the product with a 75% approval rating, while subject matter experts found it 88.89% valid. Evaluation was conducted by surveying students in plant morphology courses, with results confirming the product's effectiveness. Through AR, students can directly engage with three-dimensional (3D) models of various parts of plants, such as roots, stems, leaves, flowers, and fruits. They can manipulate and examine each part more deeply, enhancing their understanding of the relationships between parts and the functions of each component. Additionally, AR makes learning plant morphology more engaging and lifelike.Keywords: Augmented Reality; biology; Leafcapture identification.

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Plant Classification Based on Leaf Edges and Leaf Morphological Veins Using Wavelet Convolutional Neural Network

Cited by 4 publications

References 27 publications

Keystroke Dynamics Analysis Using Machine Learning Methods

Keystroke Dynamics Analysis Using Machine Learning Methods

Experimental Evaluation of Coffee Leaf Disease Classification and Recognition Based on Machine Learning and Deep Learning Algorithms

Revolutionizing Biology Learning Through Ar: The Case of Leafcapture Application Development

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