Plant species identification based on leaf venation  features using SVM

Ambarwari, Agus; Adrian, Qadhli Jafar; Herdiyeni, Yeni; Hermadi, Irman

doi:10.12928/telkomnika.v18i2.14062

Cited by 26 publications

(10 citation statements)

References 25 publications

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“…Again in a study based on leaf vein patterns, different classification methods were compared and neural networks were shown to perform best, followed by support vector machines (SVM) [4]. SVM can be seen in similar studies, 32 species classified and the average accuracy obtained in testing data is 82.67% [5]. In another study a hybrid model is used by SVM the proposed model yields to improve the identification rate up to 98.9% and 93.3% for both Flavia and Swedish dataset respectively [6].…”

Section: Introductionmentioning

confidence: 91%

Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Adak

2020

Sakarya University Journal of Computer and Information Sciences

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Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.

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Section: Introductionmentioning

confidence: 91%

Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Adak

2020

Sakarya University Journal of Computer and Information Sciences

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“…SVM works very well on high-dimensional data sets [23]. This method uses a kernel technique that maps original data from the originating dimension to another relatively higher dimension [24]. In the NN method, the training process studies all training data, whereas SVM only studies selected data used in classification [25].…”

Section: Support Vector Machinementioning

confidence: 99%

Orchid types classification using supervised learning algorithm based on feature and color extraction

et al. 2021

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Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.

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“…In [21], a modified local binary pattern was proposed to extract texture features, and a simple nearest neighbor classifier was performed for classification, to decrease the intra-class variation the clustering was exploited in order to group symmetric leaf samples; the results prove that considering texture features alone is not sufficient. In [22], the authors propose to classify plant species using 19 leaf venation features using a support vector machine (SVM) with an RBF kernel. In [23], the authors propose to identify plant leaf based on visual features using different artificial intelligence techniques such as artificial neural networks, the naive Bayes algorithm, the random forest algorithm, the K-nearest neighbor (KNN), and the support vector machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

et al. 2021

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In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.

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Plant species identification based on leaf venation features using SVM

Cited by 26 publications

References 25 publications

Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Orchid types classification using supervised learning algorithm based on feature and color extraction

Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

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