Classification of Leaf Type Using Multilayer Perceptron, Naive Bayes and Support Vector Machine Classifiers

Musić, Azra; Gagula-Palalic, Sadina

doi:10.21533/scjournal.v5i2.119

Cited by 5 publications

(7 citation statements)

References 3 publications

(5 reference statements)

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“…Studies Flavia dataset [49] [8], [17], [23], [25], [29], [36], [43] ICL leaf dataset [22] [46] LeafSnap dataset [21] [19] Own-authored dataset [1], [3], [10], [13], [26], [29], [35], [42] UCI Iris dataset [9] [26] UCI Leaf dataset [38] [2], [27], [30], [41], [51], [44] UCI One-hundred plant species leaves dataset [26] [20], [51] Uninformed [4], [31] We realized that a wide variety of datasets was used in the selected studies, and many authors chose to create their own datasets (8 studies of 25), possibly by not finding available datasets with the desired characteristics or information. Even so, public datasets were also widely used, with Flavia dataset [49], and UCI Leaf Dataset [38] being the most used (7 and 6 studies of 25, respectively).…”

Section: Datasetmentioning

confidence: 99%

“…Some studies [1,10,19] used leaves from Acer species plants to illustrate samples of the datasets used or to illustrate some steps taken in their approaches. The species Acer Palmatum is mentioned in the following works, as an example of the species available on the dataset used: [2], [3], [13], [20], [23], [25], [26], [27], [29], [30], [31], [35], [36], [44], [41], [42], [43], [46], [51].…”

Section: Datasetmentioning

confidence: 99%

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Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

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Plant species identification is a task of interdisciplinary interest,desirable in many contexts, such as gardening, botanical research,and agriculture. For some plant species such as the Acer Palmatum,the characteristics of leaves, petioles, and trunks can drasticallyvary among the different genera of the same subspecies. ComputerVision and Machine Learning research areas made possible thecreation of different classifiers trained to assist in species plantrecognition based on digital images. However, the success of thetraining of a classification model is directly linked to the qualityand adequacy of the dataset used. For the classification of AcerPalmatum plants, datasets composed of samples regarding the differentvarieties within this genus were not identified. Thus, in thispaper, we proposed the creation of a new dataset and of a classifierto support the identification of distinct plant genera of the subspeciesAcer Palmatum. We believe that our proposal aggregatesrelevant information not currently available, and will encouragefurther work aimed at automatically classifying between genera ofsome plant species which task is considered non-trivial even forexperienced growers.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Ascari

Casanova

Fávero

et al. 2023

Anais Do XIV Computer on the Beach - COTB'23

View full text Add to dashboard Cite

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“…Ketika semua error telah ditemukan, gradient descent dihitung guna meminimalkan cost function dan menemukan solusi optimal [4].…”

Section: Multilayerunclassified

Pengaruh Perbedaan Jumlah Hidden Layer dan Node pada Hidden Layer Terhadap Performa Model Klasifikasi Diabetes

Suprana

2022

KONSTELASI

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Diabetes merupakan salah satu penyakit kronis yang serius. Diabetes umumnya ditandai dengan tubuh tidak membuat cukup insulin atau tidak dapat menggunakan insulin yang dibuat seefektif yang dibutuhkan. Diabetes dapat dikelompokkan menjadi empat tipe. Semua tipe diabetes dapat menyebabkan komplikasi pada berbagai bagian tubuh dan dapat meningkatkan risiko kematian dini. Pada paper ini, dilakukan penelitian dalam membangun model menggunakan algoritma multilayer perceptron di WEKA untuk mengklasifikasikan seseorang menderita diabetes. Dataset yang digunakan adalah “Diabetes Health Indicators Dataset” yang bersumber dari Kaggle. Terdapat tujuh percobaan yang dilakukan yang mana hasil terbaik ditunjukkan oleh percobaan 7 berdasarkan parameter performa F-measure.

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“…k-Nearest Neighbor (k-NN) is a supervised classification that can classify non-attribute by assigning them to a similar attribute in the class, as shown in Fig. 3 [26]. Based on the articles related to k-NN, the probability of error of simple classification rule is bounded with the Bayes minimum probability of error is better that make the most impact paper in pattern recognition and texture classification applications such as the document authentication texture features [27].…”

Section: K-nearest Neighbormentioning

confidence: 99%

Comparative analysis of classification techniques for leaves and land cover texture

Azmer

Hassan

Khaleefah

et al. 2021

Int. J. Adv. Intell. Informatics

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The texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.

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Classification of Leaf Type Using Multilayer Perceptron, Naive Bayes and Support Vector Machine Classifiers

Cited by 5 publications

References 3 publications

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Developing an automatic classifier of different plant genera of the subspecies Acer Palmatum

Pengaruh Perbedaan Jumlah Hidden Layer dan Node pada Hidden Layer Terhadap Performa Model Klasifikasi Diabetes

Comparative analysis of classification techniques for leaves and land cover texture

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