Classification of medicinal plants: An approach using modified LBP with symbolic representation

Naresh, Yalagala; Nagendraswamy, H. S.

doi:10.1016/j.neucom.2015.08.090

Cited by 115 publications

(47 citation statements)

References 19 publications

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“…For this reason, researchers in computer vision have used leaves as a comparative tool to classify plants [7,8,9,10]. Characters such as shape [11,12,13], texture [14,15,16] and venation [17,18] are the features most generally used to distinguish the leaves of different species. The history of plant identification methods, however shows that existing plant identification solutions are highly dependent on the ability of experts to encode domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

460

172

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Highlights• A deep learning approach to quantify discriminatory leaf is proposed.• Shape is not a dominant feature for leaf but rather the different orders of venation.• Deep learning reveals transformation of leaf features from general to specific types.• Findings archived fit with the hierarchical botanical definitions of leaf characters• Features learned using deep learning can improve plant recognition performance. AbstractPlant identification systems developed by computer vision researchers have helped botanists to recognize and identify unknown plant species more rapidly. Hitherto, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Deconvolutional Network (DN) approach. We report somewhat unexpected results: (1) different orders of venation are the best representative features compared to those of outline shape, and (2) we observe multi-level representation in leaf data, demonstrating the hierarchical transformation of features from lower-level to higher-level abstraction, corresponding to species classes. We show that these findings fit with the hierarchical botanical definitions of leaf characters. Through these findings, we gained insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems. The source code and models are available at: https://github.com/cs-chan/Deep-Plant.

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Section: Introductionmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

460

172

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show abstract

“…Fig.14 compares accuracies of approach [12] (blue) with current approach (red). Hierarchical clustering used in [5] clusters the leaf images depending on the similarity of the texture. If the image of the leaf does not contain any prominent structure or if the quality of the scanned data is poor, then this method of clustering the images depending the texture does not produce better recognition rates.…”

Section: Discussionmentioning

confidence: 99%

“…Overall accuracy is 47.2%. Fig.15 compares accuracies of approach [5] (blue) with current approach (red). Fig.15.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…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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Classification of medicinal plants: An approach using modified LBP with symbolic representation

Cited by 115 publications

References 19 publications

How deep learning extracts and learns leaf features for plant classification

How deep learning extracts and learns leaf features for plant classification

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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