Factors influencing the use of Deep Learning for Medicinal Plants Recognition

Anchitaalagammai, J. V.; Revathy, J S Shantha Lakshmi; Kavitha, S; Murali, S.

doi:10.1088/1742-6596/2089/1/012055

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…In order to complete the task, ResNet50 was utilised in this work [10]. The architecture has been validated through the use of four different leaf datasets, one of which is a self-created dataset consisting of leaf images collected from the internet.…”

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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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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“…Many algorithms have been developed to identify plant species using leaf shape features. Furthermore, various methods have been applied in research on plant species classification, including herbal plants [15]- [18], Shorea type [19], tropical trees [20], and types of fruit [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Identification of mangrove tree species using deep learning method

Asnur

Kosasih

Madenda

et al. 2023

IJAAS

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Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including <em>Avicenia marina, Avicenia officialis, Rizophora apiculata, </em>and <em>Soneratia caseolaris</em>.

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