Classification of Plant Leaf Using Shape and Texture Features

Sujith, A. V. L. N.; Neethu, R.

doi:10.1007/978-981-15-7345-3_22

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Pacifico et al [17] proposed an automatic plant classification system based on color and texture features using a multi-layer perception with backpropagation (ML-BP) classifier. Sujith and Neethu [18] proposed a feature combination method to classify plants using ANN classifier by combining shape and texture features. Ahmed et al [19] proposed six color features and twenty-two texture features (GLCM) have been calculated.…”

Section: Preliminary Studymentioning

confidence: 99%

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Ariyapadath¹

2021

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The main purpose of this research work is to apply machine learning and image processing techniques for plant classification efficiently. In the plant classification system, the conventional method is time-consuming and needs to apply expensive analytical instruments. The automated plant classification system helps to predict plant classes easily. The most challenging part of the automated plant classification research is to extract unique features of leaves. This paper proposes a plant classification model using an optimal feature set with combined features. The proposed model is used to extract features from leaf images and applied to image classification algorithms. After the evaluation process, it is found that GIST, Local Binary Pattern and Pyramid Histogram Oriented Gradient have better results than others in this particular application. Combined these three features extraction techniques and selected the optimal feature set through Neighbourhood Component Analysis. The optimal feature set helps classify plants with maximum accuracy in minimal time. Here performed an extensive experimental comparison of the proposed optimal feature set and other feature extraction methods using different classifiers and tested on different data sets (Swedish Leaves, Flavia, D-Leaf). The results confirm that this optimal feature set with NCA using ANN classifier leads to better classification achieved 98.99% accuracy in 353.39 seconds.

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Section: Preliminary Studymentioning

confidence: 99%

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Ariyapadath¹

2021

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“…In addition, There are studies in which classification is made with different methods using feature vectors extracted from the trained convolutional neural network called pre-trained models (Lee et al, 2017;Beikmohammadi & Faez, 2018;Wang et al, 2018;Raj & Va jravelu, 2019). Different classification methods such as logistic regression (LR) , support vector machine (SVM), Naive Bayesian, linear discriminant analysis (LDA), radial fundamental probabilistic neural network (RF-PNN), multilayer perceptron (MP), AdaBoost, probabilistic neural network have been used (Silva et al, 2013;Jiang et al 2013;Padao & Maravillas, 2015;Mostafa et al, 2020;Sujith & Neethu, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Swedish Leaf dataset has been used by Hewitt & Mahmoud (2018), Zhang et al (2020), Tsolakidis et al (2014), Sujith & Neethu (2021), Atabay (2016), Anubha Pearline et al (2019 to develop classification model. SVM has been used by Hewitt & Mahmoud (2018), Zhang et al (2020), Tsolakidis et al (2014).…”

Section: Introductionmentioning

confidence: 99%

“…SVM has been used by Hewitt & Mahmoud (2018), Zhang et al (2020), Tsolakidis et al (2014). Sujith & Neethu (2021), Atabay (2016), Anubha Pearline et al (2019 have used MLP, CNN and pre-trained model respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The Flavia dataset was used by Wu et al (2007), Kumar et al (2012), Kulkarni et al (2013), Kadir et al (2013), Tsolakidis et al (2014), Lavania & Matey (2014), Wang et al (2014), Atabay (2016), Shah et al (2017), Barre et al (2017), Lee et al (2017), Anubha Pearline et al (2019, Hewitt and Mahmoud (2018), Beikmohammadi & Faez (2018), Raj & Vajravelu (2019), , Zhang et al (2020), Mostafa et al (2020) Sujith & Neethu (2021), with different classification models such as KNN PNN SVM CNN etc.…”

Section: Introductionmentioning

confidence: 99%

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Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models

CAMGÖZLÜ,

KUTLU

2023

Natural and Engineering Sciences

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It is important to identify a high-performance model that can classify all leaves and even differentiate according to regional variations of the same leaf type. In this study, a leaf classification model was created using 5 different datasets with different number of images and compared with models. For this purpose, 4 different pre-trained models called VGG16, InceptionV3, MobileNet and DenseNet are used. In addition, a new model was proposed and model training was carried out using these datasets . Using the all models, inputs are transformed into feature vectors by parameter transfer method and used for classification with the nearest neighbor algorithm and support vector machine. The performance of the classifications were compared with similar studies in the literature.

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Deep learning-based aquatic plant recognition technique and natural ecological aesthetics conservation

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2024

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Classification of Plant Leaf Using Shape and Texture Features

Cited by 4 publications

References 13 publications

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models

Deep learning-based aquatic plant recognition technique and natural ecological aesthetics conservation

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