Advanced Medicinal Plant Classification and Bioactivity Identification Based on Dense Net Architecture

Pukhrambam, Banita; Sahayadhas, Arun

doi:10.14569/ijacsa.2022.0130614

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…There are about 8,000 herbal remedies used in these traditional medicines. As per surveyed findings, approximately 75% of migrants utilize herbal plants for medicinal reasons [2,3].…”

Section: Introductionmentioning

confidence: 76%

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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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“…There are about 8,000 herbal remedies used in these traditional medicines. As per surveyed findings, approximately 75% of migrants utilize herbal plants for medicinal reasons [2,3].…”

Section: Introductionmentioning

confidence: 76%

“…Homogeneity of the pixel is computed for the seed pixel to verify the presence of unfermented pixels in the neighborhood of the remaining pixel in the particular region. The computation rule is as follows in (2).…”

Section: B Region Growing Segmentationmentioning

confidence: 99%

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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show abstract

“…However, identifying medicinal plants without knowing their class doesn't serve our study. Similar work was done by Pukhrambam et al in which they trained a DenseNet-based CNN for distinguishing Medicinal Plants from phytochemistry and therapeutics plants [27]. Berihu et al proposed a GoogLeNet-based method to classify medicinal plants from Ethiopia with 96.7% accuracy [28].…”

Section: Related Workmentioning

confidence: 88%

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Islam,

Rahman,

Hossain

et al. 2024

IEEE Access

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Medicinal plants are essential to healthcare since ancient times and are integral to developing drugs and other medical treatments. More than 25% of medicines in developed countries are produced from medicinal plants, while in developing countries, approximately 80% of individuals receive primary healthcare from these plants. Traditionally, these plants are identified manually by experts, which is tedious, time-consuming, subjective and dependent on the availability of experts. Furthermore, a wrong detection can result in serious health issues or death. This signifies the need for a more reliable approach to identifying medicinal plants, which is accurate and practical. Several automated methods were proposed previously, utilizing deep learning and traditional machine learning (TML) techniques, but they require singular leaf images and failed to achieve sufficient accuracy when demonstrated in a different setting. Capturing singular leaf images for each plant is also time-consuming and laborious. This paper presents a robust, accurate and practical system to identify medicinal plants from smartphone-captured plant images in the site of plants.The proposed system utilized a cascaded architecture to extract features using a pre-trained ResNet50 model, which were optimized using Particle Swarm Optimization (PSO) to classify the plants using a Support Vector Machine (SVM). The proposed ResNet50-PSO-SVM network classified seven medicinal plants with 99.60% accuracy, outperforming the state-of-the-art (99%). The system was demonstrated for three different smartphones, classifying an image in 0.15 seconds with 97.79% accuracy on average. The system's high accuracy, rapid identification time and robustness ensured its practical use.

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“…Furthermore, [33] utilized EfficientNetB4 models, both regular and pre-trained, to classify 38 plant species, recording a 99% accuracy. In [59], a DenseNet-based CNN was utilized for medicinal plant classification in Manipur, yielding a 99.56% accuracy on the IMPPAT dataset. This research also introduced the Ensemble Deep Learning-Automatic Medicinal Leaf Identification (EDL-AMLI) classifier, which, based on weighted model outputs, surpassed established pre-trained models like MobileNetV2, InceptionV3, and ResNet50, achieving a remarkable 99.9% accuracy [60].…”

Section: Discussionmentioning

confidence: 99%

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

Kiflie,

Sharma,

Haile

et al. 2024

Computers

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Ethiopia is renowned for its rich biodiversity, supporting a diverse variety of medicinal plants with significant potential for therapeutic applications. In regions where modern healthcare facilities are scarce, traditional medicine emerges as a cost-effective and culturally aligned primary healthcare solution in developing countries. In Ethiopia, the majority of the population, around 80%, and for a significant proportion of their livestock, approximately 90% continue to prefer traditional medicine as their primary healthcare option. Nevertheless, the precise identification of specific plant parts and their associated uses has posed a formidable challenge due to the intricate nature of traditional healing practices. To address this challenge, we employed a majority based ensemble deep learning approach to identify medicinal plant parts and uses of Ethiopian indigenous medicinal plant species. The primary objective of this research is to achieve the precise identification of the parts and uses of Ethiopian medicinal plant species. To design our proposed model, EfficientNetB0, EfficientNetB2, and EfficientNetB4 were used as benchmark models and applied as a majority vote-based ensemble technique. This research underscores the potential of ensemble deep learning and transfer learning methodologies to accurately identify the parts and uses of Ethiopian indigenous medicinal plant species. Notably, our proposed EfficientNet-based ensemble deep learning approach demonstrated remarkable accuracy, achieving a significant test and validation accuracy of 99.96%. Future endeavors will prioritize expanding the dataset, refining feature-extraction techniques, and creating user-friendly interfaces to overcome current dataset limitations.

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Advanced Medicinal Plant Classification and Bioactivity Identification Based on Dense Net Architecture

Cited by 4 publications

References 17 publications

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

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

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

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