An Extensive Study on Machine Learning Paradigms Towards Medicinal Plant Classification on Potential of Medicinal Properties

Sapna, R.; Sheshappa, S. N.

doi:10.1007/978-3-031-12413-6_43

Cited by 2 publications

(2 citation statements)

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“…As a critical problem for the preservation, authenticity, and production of herbal medicines, the automatic classification of medicinal plants needs further study [22]. Owing to the difficulties due to illumination effects, shadow effects, nonlinear seed points, overfitting issues and accumulation of reconstruction error in the processing of medicinal plant datasets, the identification and classification of plant leaves will face significant challenges in employing machine learning algorithms.…”

Section: B Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Global Pattern Feedforward Neural Network Structure with Bacterial Foraging Optimization towards Medicinal Plant Leaf Identification and Classification

Sapna¹,

Sheshappa²,

Vijayakarthik³

et al. 2022

IJACSA

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Medicinal Plant species help to cure various diseases across the world. The automated identification of medicinal plant species to treat disease based on their structure is much required in pharmaceutical laboratories. Plant Species with a complex background in the field will make the detection and classification more difficult. In this paper, optimization of bacterial foraging technique has been employed towards medicinal plant prediction and classification architecture based on feed-forward neural network. It is capable of identifying both complex structures of medicinal plants. Feed-forward Neural Networks are considered to have good recognition accuracy compared to other machine learning approaches. Further bacterial foraging has been implemented to minimize the feature search space to the classifier and provides optimal features for the plant classification. The experimental outcomes of the proposed approach has been analysed by employing the medley dataset and evaluating the performance of the proposed approach with respect to dice similarity coefficient, Specificity and sensitivity towards medicinal plant classification. The findings are very positive, and further research will focus on using a large dataset and increased computing resources to examine how well deep-learning neural networks function in identifying medicinal plants for use in health care.

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Section: B Problem Statementmentioning

confidence: 99%

“…Bacterial Optimization computes the optimal features to classify the plant into categories, especially it is found effective to classify various patterns. The optimal feature set has to be determined using the following fitness function (22).…”

Section: E Bacterial Foraging Optimizationmentioning

confidence: 99%