Classification of Immunity Booster Medicinal Plants Using CNN: A Deep Learning Approach

Musa, Md. Nor; Arman, Md. Shohel; Hossain, Md. Ekram; Thusar, Ashraful Hossen; Nisat, Nahid Kawsar; Islam, Arni

doi:10.1007/978-3-030-81462-5_23

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Valdez et al also utilized Mo-bileNet and achieved 97.43% accuracy [18]. Raisa et al [19] and Musa et al [20] [22]. The model was trained using 2400 images and yielded 96.76% accuracy.…”

Section: Related Workmentioning

confidence: 99%

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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“…Valdez et al also utilized Mo-bileNet and achieved 97.43% accuracy [18]. Raisa et al [19] and Musa et al [20] [22]. The model was trained using 2400 images and yielded 96.76% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Islam,

Rahman,

Hossain

et al. 2024

IEEE Access

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“…There were 15 different plant species used in this study, and the results showed that 74% accuracy. This research [15] suggests a machine learning-based system for categorising the leaves of medicinal plants. An enhanced medicinal plant leaves dataset is used to test out various machine learning models, including multi-layer perceptron, bagging, simple logistic, random forest, and logit-boost.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

Bhelkar¹,

Sharma²

2022

ijhs

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The Indian medical practise of Ayurveda has gained international renown. Herbal preparations are the basis of Ayurveda medicine. The pharmaceutical industry is beginning to pay more attention to medicinal plants because they have fewer adverse effects and reactions than modern medicine and are also less expensive. In recent years, numerous Deep learning, machine learning algorithms that are both effective and reliable have been utilised for plant classifications by using images of leaf. In this work, 45 distinct medicinal plant leaves were used, and a deep learning model was applied in order to achieve a high degree of accuracy in the classification and recognition procedures that were carried out with the help of computer vision techniques. After categorising the leaves of numerous medicinal plants, the Xception model has a 97.65% accuracy rate.

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“…However, due to the large number of medicinal plant species and their morphological similarity, the identification process can lead to errors that hurt the user and can even be fatal. The manual identification process takes a long time and requires assistance from experts [4]. Therefore, technology is needed to help identify the type of drug.…”

Section: Introductionmentioning

confidence: 99%

Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network

Latumakulita,

Mandagi,

Paat

et al. 2022

Bull.Socinf.The.App

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Indonesia has a variety of medicinal plants that are efficacious for preventing or treating various diseases. Each region has unique medicinal plants, such as in North Sulawesi, there are many medicinal plants with local names of "Jarak" (Jatropha curcas), "Jarak Merah" (Jatropha multifida), "Miana" (Coleus Scutellarioide), and "Sesewanua" (Clerodendron Squmatum Vahl). This research applies the Convolutional Neural Network (CNN) method to identify the types of medicinal plants of North Sulawesi based on leaf images. Data was collected directly by taking photos of medicinal plant leaves and then using the augmentation process to increase the data. The first stage is conducting training and validation processes using 10-fold cross-validation, resulting in 10 classification models. Evaluation results show that the lowest validation accuracy of 98.4% was obtained from fold-4, and the highest was 100% from fold-2. The third stage was to run the testing process using new data. The results showed that the worst model produced a test accuracy of 80.91% while the best model produced an accuracy of 87.73% which means that the identification model is quite good and stable in classifying types of medicinal plants based on its leaf images. The final stage is to develop a web-based system to deploy the best model so people can use it in real-time

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Classification of Immunity Booster Medicinal Plants Using CNN: A Deep Learning Approach

Cited by 4 publications

References 11 publications

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Identification and classification of medicinal plants using leaf with deep convolutional neural networks

Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network

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