Identification of the Plants Based on Leaf Shape Descriptors

Salve, Pradip; Sardesai, Milind; Manza, Ramesh R.; Yannawar, Pravin

doi:10.1007/978-81-322-2517-1_10

Cited by 26 publications

(11 citation statements)

References 10 publications

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“…The accuracy of the HOG and Hu invariant moments descriptor achieved in this study were 84.70% and 25.31% respectively. In the study presented by Salve et al (2016) , the implementation of the HOG and Zernike moments descriptor were proposed. This study used the subset Visleaf dataset which contained 50 plant species and 10 samples for each of species, which is a total of 500 images.…”

Section: Discussionmentioning

confidence: 99%

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Murat

Chang

Abu

et al. 2017

PeerJ

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Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

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Section: Discussionmentioning

confidence: 99%

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Murat

Chang

Abu

et al. 2017

PeerJ

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“…Fig.15. Accuracies using approach [5] (blue) and current approach (red) In [14] the Zernike moment and HoG method is used as shape descriptor for the classification of leaves. HoG depends largely on the block size, cell size and number of orientation bins, which tend to be different for different shapes.…”

Section: Discussionmentioning

confidence: 99%

“…Overall accuracy is 27.4%. Fig.16 compares accuracies of approach [14] (blue) with current approach (red). Fig.16.…”

Section: Discussionmentioning

confidence: 99%

“…Fig.16. Accuracies using approach [14] (blue) and current approach (red) In [15] the Moment Invariant, convexity, perimeter ratio, multi scale distance ratio, average margin distance, and margin statistics is used for the classification of leaves using LDC. As all the features are shape features, the leaf images with similar shapes may not be handled by this approach.…”

Section: Discussionmentioning

confidence: 99%

“…Accuracy (%) Approach of [1] 29.8 Approach of [12] 23.02 Approach of [5] 47.2 Approach of [14] 27.4 Approach of [15] 48.1 Approach of [4] 13.8 Current Approach 94 VI. CONCLUSIONS AND FUTURE SCOPE This article proposes a method of utilizing different leaf types differing in shape.…”

Section: Approachmentioning

confidence: 99%

See 2 more Smart Citations

Template based Shape Feature Selection for Plant Leaf Classification

Chaki¹

2017

IJET

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Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

Kumar¹,

Sumithra

Saranya³

2019

Concurrency and Computation

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Summary This paper proposes an automatic classification technique that uses leaf images some medicinal plants. It is primarily the core reason that drives the research presented here, including the introduction of new innovative segmentation and classification techniques that are deployed to facilitate automatic detection. The major aim of the work is to introduce a new leaf disease prediction technique. The study conducted here a unique but effective image segmentation, feature extraction, as well as plant leaf disease classification. The proposed approach initially preprocesses leaf images of plants thereafter which the diseased sections of the plant are segmented by deploying Particle Swarm Optimization (PSO)–based fuzzy c means segmentation (PSO‐FCM), Gaussian Mixture Model (GMM)–based background subtraction. Vein and shape features, edge‐based feature extraction, and texture characteristics or texture features (TF) are computed. This methodology classifies the leaves of medicinal plants by deploying the Multiple Kernel Parallel Support Vector Machine (MK‐PSVM) classifier. The classifier is implemented via the use of MATLAB classifier. The results are measured using the accuracy, sensitivity, specificity, precision, and F‐measure metrics. Experimental results depict that the classifiers that have been proposed here achieve a higher classification accuracy enabling leaf detection.

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Identification of the Plants Based on Leaf Shape Descriptors

Cited by 26 publications

References 10 publications

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Template based Shape Feature Selection for Plant Leaf Classification

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

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