The Plant Leaf Classification System using an Optimum Feature Selection by Grey Wolf Optimization.
Distinguishing and understanding various plants species is of most extreme significance for keeping up biodiversity. Leaf image classification is a pivotal errand since leaves are showing high intra class variety in the leaf features like shape, color and texture. There is a need to build a classifi...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 138 - 150 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151005948&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151005948 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151005948 151005948 151005948 151005948 ppf: 138 ppct: 12 formats: fmt: @attributes: type: P tig: atl: The Plant Leaf Classification System using an Optimum Feature Selection by Grey Wolf Optimization. aug: au: Dudi, Bhanu Prakash Rajesh, V. affil: Research Scholar, Department of ECE, K L Deemed to be University, Vaddeswaram, Guntur, A.P., India sug: subj: Plant Leaves Classification Bioinformatics Methods Validity Plant Leaves Anatomy and Histology Machine Learning Algorithms Support Vector Machine Image Processing, Computer Assisted Models, Statistical Random Forest ab: Distinguishing and understanding various plants species is of most extreme significance for keeping up biodiversity. Leaf image classification is a pivotal errand since leaves are showing high intra class variety in the leaf features like shape, color and texture. There is a need to build a classifier to recognize the plants adequately. In this paper, we proposed a new feature selection method using Grey Wolf Optimizer (GWO) technique for plant leaf classification system. Selection of features prunes the data set by choosing a subset of appropriate features from a wide pool, avoiding problems such as over fitting, poor performance and cost of computation. Selection of features refers to the process by which a subset of specific features is chosen from a pool of features that are initially available. These selected features are given to three distinct machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM), and Random Forest to classify the leaves. To assess the performance of classifiers we tested on publically accessible dataset, called Flavia. From the experimental results it is shown our proposed GWO is better than Particle Swarm Optimization (PSO) algorithm in terms of number of selected features, accuracy, precision recall, and F-measure. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|