Identification of ayurvedic medicinal plants by image processing of leaf samples

Kumar, P. Manoj; Surya, C. M.; Gopi, Varun P.

doi:10.1109/icrcicn.2017.8234512

Cited by 39 publications

(6 citation statements)

References 9 publications

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“…The most famous architecture for classifying images using visual data is CNN. Deep learning methods use convolutional layers for automatic feature extraction [8] . Medicinal plant identification and classification are performed using image processing, machine learning, and computer vision techniques [3] .…”

Section: Data Descriptionmentioning

confidence: 99%

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Bangladeshi medicinal plant dataset

Borkatulla,

Ferdous,

Uddin

et al. 2023

Data in Brief

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Section: Data Descriptionmentioning

confidence: 99%

“…A medicinal plant is considered an important source of prevention against various diseases [2] . The essential medicine component is extracted from different parts of the plants [8] . In underdeveloped countries, people use medicinal plants as a substitute for medicine.…”

mentioning

confidence: 99%

Bangladeshi medicinal plant dataset

Borkatulla,

Ferdous,

Uddin

et al. 2023

Data in Brief

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“…For this, many factors are taken into account varying from plant length, appearance, leaf color, petals, stem, and root. It is realized in the given study that the plant's leaf is self-sufficient to provide reasonable details of the plants' complete health [6], [7]. Plant leaves are available throughout the year, are easier to be photographed digitally, and simpler to enumerate in two dimensions.…”

Section: Introductionmentioning

confidence: 99%

Techniques of deep learning and image processing in plant leaf disease detection: a review

Kini

Prema

Pai

2023

IJECE

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<span lang="EN-US">Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold <span>the ophthalmic functionality of the human eye. In agriculture, leaf examination</span> is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.</span>

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“…Expert taxonomists are very less available to this area. In contrast, ayurvedic remedies are preferred by people over all others [2]. Additionally, plants are classified according to their characteristics by taxonomists, including their leaves, flowers, stems, and branches.…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Learning and Machine Learning Algorithms for The Identification of Medicinal Plant Leaves Based on Their Spectral Characteristics

Tiwari,

Gupta,

Prakash

et al. 2023

J. Adv. Zool.

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The study and consideration of medicinal plants have been ongoing throughout history due to their significant role in maintaining the well-being of mammals. Although identifying medicinal plants can be a valuable skill, it is often time-consuming, tedious, and requires the expertise of a specialist. The project works on the technique of image processing, which identifies the various medicinal plants. There has been a strong emphasis on improving efficiency through the application of technology, with a focus on incorporating digital image processing and pattern recognition techniques. To ensure accurate plant identification, proposals involving the application of computer vision neural network techniques have been advanced. This approach involves neural network models such as CNN, SVM, KNN, and Navie Bay for identifying the medical plants based on their respective features. After the validation step, the project provides a classification of 92.3 precision and 90.56 F1 score.

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Identification of ayurvedic medicinal plants by image processing of leaf samples

Cited by 39 publications

References 9 publications

Bangladeshi medicinal plant dataset

Bangladeshi medicinal plant dataset

Techniques of deep learning and image processing in plant leaf disease detection: a review

Applying Deep Learning and Machine Learning Algorithms for The Identification of Medicinal Plant Leaves Based on Their Spectral Characteristics

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