Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model

Eid, Heba F.; Abraham, Ajith

doi:10.3233/his-180248

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…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]. Automatic plant detection proposed by using SVM again and they reached 91.25% accuracy [7].…”

Section: Introductionmentioning

confidence: 84%

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: 84%

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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“…Chopra, Kumar and Mehta [46] hybridized PSO and GWO and used the new approach to provide solution to convex economic load dispatch problem. The combination of PSO, GWO and SVM was used by Eid and Abraham [47] to identify different species of plants.…”

Section: Optimal Power Dispatch Problemsmentioning

confidence: 99%

Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

Dada

Bassi

Oyewola

et al. 2022

Gazi University Journal of Science

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“…To evaluate the performance of our proposal in classification, we compare our proposal with eight different paradigms. These are ''color moments + gray level + Naive Bayes classifier [45],'' ''Co-ccurrence + gray level + Random forest classifier [45],'' ''Run Length + LBP-GWO + J48 classifier [45],'' ''PSO-segmentation + LBP-GWO + SVM [46],'' ''ZM + Hog + SVM [47],'' ''CNN [48]'' and ''CNN + SVM [48]''.…”

Section: ) Comparison Algorithmsmentioning

confidence: 99%

Contour Detection by Simulating the Curvature Cell in the Visual Cortex and its Application to Object Classification

Chen

Cai

2020

IEEE Access

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This paper addresses contour detection by simulating the human visual system and its application to visual object classification. Unlike previously designed bioinspired contour detection algorithms, we consider contour to be the salience of an edge image, and we extract the salience by simulating the endstopped cell and curvature cell in the visual cortex. Generally, we follow a local-to-global feed-forward architecture, in which the size of the receptive field (RF) increases from the primary visual cortex to the higher visual cortex. Edges are first detected by simple cells in small RFs, where textural details are suppressed by non-classical receptive fields (NCRFs) and sparse coding. Second, edges are integrated into local segments by complex cells. Afterwards, they are combined into the salience of edge images by endstopped cells and curvature cells and are ultimately the core of the final contour. In addition, we also apply the bioinspired contour detection algorithm to visual object classification tasks. Experiments on contour extraction show that, compared with state-of-the-art bioinspired algorithms, our algorithm makes a considerable improvement on contour detection. Experiments on visual object classification show that the contours produced by our proposal are powerful representations of the original images, which implies that our proposal is both biologically plausible and technologically useful. INDEX TERMS Contour detection, curvature cell, image processing, machine vision, biologically inspired computation, visual object classification.

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Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model

Cited by 14 publications

References 19 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

Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

Contour Detection by Simulating the Curvature Cell in the Visual Cortex and its Application to Object Classification

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