Classification Of Medicinal Plants Research Articles

Medicinal Plant Classification and it's Identification

Indian Ayurvedic Medicine reached international level The basis of Ayurveda is herbal medicine. The pharmaceutical industry is starting to pay more. Be careful with herbs because their side effects and consequences are less than modern medicine and at the same time they are very expensive. The company efficiently and reliably uses leaf images from many deep and machine learning algorithms developed in recent years. In this study, leaves of 45 different medicinal plants were used and the deep learning method was used. A number of classification and identification methods were used using computer vision technology to achieve the best results. There are many medicinal plants according to the classification of the paper plant

Diagnosing Medicinal Plants and their Fungal Diseases Using Deep Learning Models

Today, with the development of technology, most manual methods have been replaced by automated computer systems for human convenience. Plant identification and disease classification are two major areas of agricultural research and are aimed at introducing computerized systems instead of manual methods. Millions of plant species are in the world and play a significant role in human life. Among all the types of plants, medicinal plants play an essential role in the traditional medical field because herbal plants can heal humans. Currently, there is a reactivation of interest in herbal medicines at the global level, and conventional medicine is now accepting the use of medicines and their products once they have been scientifically validated. To achieve this goal, we evaluated the performance of two common pre-trained deep learning models (VGG19 and ResNet50) and compared their accuracy levels Finally, the system can estimate some performance metrics such as accuracy and error rate for both algorithms and compare the algorithms based on accuracy in the form of graph. These results are promising, as they show that machine-learning techniques could be used for the early identification of medicinal plants. Our algorithm has been able to achieve a high level of accuracy in the classification of medicinal plants in training and test sets, making it a potentially valuable tool for fast and accurate diagnosis in clinical environments. Keywords—VGG19, ResNet50, Traditional medicine, Medicinal Plant Identification and Disease Classification

Identification and Classification of Medicinal Plants using Deep Learning

The accurate identification of medicinal plants is crucial for ensuring the quality and efficacy of herbal remedies. This paper investigates the application of deep learning for automatic medicinal plant classification using leaf images. We propose a deep learning model based on the pre-trained ResNet-50 architecture to classify medicinal plants from the LeafSnap dataset. The model leverages transfer learning to exploit pre-trained features and fine-tune them for the specific task of medicinal plant identification. We evaluate the performance of the proposed model and achieve a high accuracy of approximately 99.86%. This demonstrates the effectiveness of deep learning, particularly the ResNet-50 architecture, for automated medicinal plant classification. Our findings highlight the potential of this approach for applications such as supporting field identification, streamlining herbarium workflows, and potentially aiding in the development of novel drug discovery pipelines. Keywords- Medicinal plants, Deep learning, Convolutional Neural Networks (CNNs), ResNet-50, Transfer learning, Data augmentation, LeafSnap dataset, Image classification

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Medicinal Plant Classification Using Particle Swarm Optimized Cascaded Network

Medicinal Plants Identification Using Federated Deep Learning

Over the years, scientists have discovered bioactive chemicals in many of the plants that have been traditionally utilized as medicinal medicines. However, identifying plant species based on their physical characteristics can be difficult, and misidentification can have severe consequences, such as the use of the incorrect plant as a medicine. With the advent of machine learning techniques such as deep learning and federated learning, it is now possible to develop automated systems for the precise image-based classification of medicinal plants. Nevertheless, medicinal plant classification using deep learning techniques typically requires a large amount of data, which can be challenging to acquire and manage due to privacy concerns, data ownership, and geographic reasons. Federated learning provides a solution to this issue by enabling the training of a shared model on multiple devices without requiring centralized data storage. In this work, we assess and optimize the federared learning framework using two federated learning approaches, FedAvg and FedProx, and four state-of-the-art deep learning networks for the job of categorizing medicinal plants by distributing the original training set into two forms, IID and Non-IID. Ultimately, the accuracy of the optimal federated learning system is improved by 5.65% and 14.84% over the baseline on IID data and Non-IID data, respectively. Furthermore, the study brings up a new difficult arena for the task of classifying medicinal plants using Non-IID training data.

EXTRACTION AND CULTIVATION OF MEDICINAL PLANTS IN THE HOMESTEAD FARM “DUNDURI” OF LUDZA MUNICIPALITY

The aim of the work: to evaluate the development possibilities of the economic activity of the homestead farm “Dunduri” by collecting and cultivating medicinal plants.
 The work was developed the basis on European Union and Latvian regulatory documents and guidelines applicable to the collection and processing of medicinal plants, special literature and homestead data.
 It is important to understand what steps to take, what problems to face and how will affect activities in the future in the field of harvesting medicinal plants. 
 The work examines the extraction of medicinal plants in Europe and Latvia, the guidelines for collecting and growing plants and the classification of medicinal plants, provides a description of the resources of the homestead farm “Dunduri”, examines the growing and harvesting technologies of medicinal plants and their realization possibilities, analyses the farm economic activities and development opportunities.

Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity

The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.

PLANT IDENTIFICATION USING MACHINE LEARNING

The preservation of therapeutic plants is crucial as it will help a broad variety of sectors, including medicine, botanic research, and plant taxonomy studies, among others. Existing technologies cannot imitate the range of therapeutic plant species present in India. The suggested technique facilitates the classification of medicinal plants by exploiting textural aspects crucial in leaf recognition and identification. The three key phases of the proposed technique are picture enhancement, feature extraction, and classification. The photographs of the leaves are shot using cellphones and then processed using digital image processing algorithms to extract the features that may be compared between them. Finally, the CNN classifier is employed to develop an automated classifier. Key Words: Plant identification, CNN algorithm, Image processing.

Identification of Medicinal Plants by Visual Characteristics of Leaves

Plants Leaf play a vital role in human life, they provide oxygen, food, shelter, medicine, fuel, gums and environmental protection. Many plants are rich in medicinal values and contain active ingredients for medicinal use. Because of factor such as global warming, increasing population, professional secrecy, lack of Government support for research activities and lack of awareness about medicinal plants, many useful plants species are now becoming extinct and are getting destroyed. Manual identification of medicinal plants leaf is a time-consuming process and need the help of experts for plants identification. To overcome this problem automatic identification and classification of medicinal plants is needed for greater benefit to humankind. In today’s era, the automatic identification and classification of medicinal plants leaf is an active research area in the field of image processing. Feature extraction and classification are the main steps in identification of medicinal plants and classification process which affect the overall accuracy of the classification system. This paper presents literature on image processing techniques used in identification and classification of medicinal plants and also the importance and benefits of medicinal plants in recent years.

Distribution and Classification of Medicinal Plants in Zakhikhah Area of Al-Anbar Desert

This study included the Zakhikhah area in the Al- Anbar desert, which it bounded on the north, east, and west by the Euphrates River and on the south by the Ramadi-Qaim road. Several exploratory field trips were taken to the study area. During this time, a semi-detailed area survey was carried out based on satellite imagery captured by American Land sat-7, topographic maps, and natural vegetation variance. All necessary field tools, including a digital camera and GPS device, were brought to determine the soil type and collect plant samples. All of these visits are planned to cover the entire state of Zakhikhah. All vegetation cover observations, identifying sampling sites and attempting to inventory and collect medicinal plants in the study area at all stages were recorded. The reasons for the variation in the distribution of medicinal plants in the Zakhikhah area were also presented in this study concerning their distribution sites. The total number of species collected in all stages, according to the findings of this study, was 12. The most abundant plant was the hibiscus, which accounted for 35.40% of the total area and covered 4210.8 acres. The samples were identified, named, and preserved in the University of Anbar’s College of Education for Pure Sciences/Department of Life Sciences herbarium. How to Cite: Fatin H. Al-Dulaimi, 2023. "Distribution and Classification of Medicinal Plants in Zakhikhah Area of Al-Anbar Desert." Journal of Agriculture and Crops, vol. 9, pp. 257-265.

Chemometric-guided chemical marker selection: A case study of the heat-clearing herb Scrophularia ningpoensis.

The selection of medicinal plants' chemical markers focuses on bioactivity as the primary goal, followed by the nature of secondary metabolites, their stability, and availability. However, herbal medicines are valued for their complex and holistic pharmacological effects. A correct chemical marker can be carefully selected by a systematic clarification of their chemical-biological relationships. In the current study, the multi-informative molecular networking (MIMN) approach was employed to construct the anti-inflammatory metabolomic pattern of a heat-clearing herb, Scrophularia ningpoensis Hemsl. (S. ningpoensis). The MIMN molecular families characterized by cinnamic acid glycosides showed a higher bioactivity score compared with the other two major chemical classes (iridoid glycosides and iridoid-cinnamic acid glycosides). The Global Natural Product Social Molecular Networking (GNPS) and Reaxys database were used to assist in the putative annotation of eighteen metabolites from the bioactive and non-bioactive molecular families. The anti-inflammatory validation step was based on the detection of reactive oxygen species (ROS) generation by activated human neutrophils. All compounds from the bioactive MIMN molecular families dose-dependently inhibited the total ROS generation promoted by fMLF (IC50: 0.04-0.42 μM), while the compounds from non-bioactive MIMN clusters did not show any significant anti-inflammatory effect. The ROS-dependent anti-inflammatory activity of these cinnamic acid glycosides was attributed to their oxygen radical scavenging ability. The most abundant cinnamic acid glycoside, angoroside C (IC50: 0.34 μM) was suggested to be selected as a chemical marker for S. ningpoensis. In this study, the MIMN platform was applied to assist in the chemical marker selection of S. ningpoensis. The correct selection of markers will aid in the compilation and revision of herbal monographs and pharmacopeias resulting in the precise analysis and classification of medicinal plants on a scientific basis.

Exploring Convolutional Neural Networks for Accurate Medicinal Plant Classification Using Leaf Image Features

Exploring Convolutional Neural Networks for Accurate Medicinal Plant Classification Using Leaf Image Features

No ambiguity: Chemosensory-based ayurvedic classification of medicinal plants can be fingerprinted using E-tongue coupled with multivariate statistical analysis.

Background: Ayurveda, the indigenous medical system of India, has chemosensory property (rasa) as one of its major pharmacological metric. Medicinal plants have been classified in Ayurveda under six rasas/tastes-sweet, sour, saline, pungent, bitter and astringent. This study has explored for the first time, the use of Electronic tongue for studies of rasa-based classification of medicinal plants. Methods: Seventy-eight medicinal plants, belonging to five taste categories (sweet, sour, pungent, bitter, astringent) were studied along with the reference taste standards (citric acid, hydrochloric acid, caffeine, quinine, L-alanine, glycine, β-glucose, sucrose, D-galactose, cellobiose, arabinose, maltose, mannose, lactose, xylose). The studies were carried out with the potentiometry-based Electronic tongue and the data was analysed using Principle Component Analysis, Discriminant Function Analysis, Taste Discrimination Analysis and Soft Independent Modeling of Class Analogy. Results: Chemosensory similarities were observed between taste standards and the plant samples-citric acid with sour group plants, sweet category plants with sucrose, glycine, β-glucose and D-galactose. The multivariate analyses could discriminate the sweet and sour, sweet and bitter, sweet and pungent, sour and pungent plant groups. Chemosensory category of plant (classified as unknown) could also be identified. Conclusion: This preliminary study has indicated the possibility of fingerprinting the chemosensory-based ayurvedic classification of medicinal plants using E-tongue coupled with multivariate statistical analysis.

Ethnopharmacological survey of medicinal herbs in Tolaki-Mekongga Tribe of Kolaka Regency and East Kolaka Regency, Southeast Sulawesi, Indonesia

Background: This research aims to collect and preserve ethnopharmaceutical knowledge based on the customs of the Tolaki-Mekongga tribe in treating various diseases experienced by people in the Tolaki-Mekongga tribal area. Methods: In this study, descriptive-qualitative methods have been used for the research objectives to be achieved. Qualitative methods have been used to identify plants as traditional medicines by interview, while quantitative methods have been used for the classification of medicinal plants, use of therapeutic plant organs, medicinal plant use formulations, determination of a group of plants as candidates for herbal medicine, phytopharmaca candidates, and even endemic plant as drugs candidates for tropical and infectious diseases. Results: The results obtained 158 medicinal plant species, although most of these medicinal plants were used by other tribes, and the Tolaki-Mekongga tribe only uses as many as ten medicinal plant species as local wisdom in the region. According to the survey results, it was found that the parts of the plant that were commonly used were roots, root water, stems, leaves, flowers, seeds, fruits and rhizomes. The processing of plants used as medicinal plants generally still uses traditional methods. Based on the questionnaires and field surveys, several things were found: through questionnaire data, as many as 133 respondents obtained online and offline were identified 158 plant species used by the Tolaki-Mekongga tribe to treat 112 types of diseases. A literature search shows that among the 158 plant species, several plants are not only used by the Tolaki-Mekongga people but also by other tribes in Indonesia and the world, but this study provides essential information and found that ten medicinal plant species are only used by the species of Tolaki-Mekongga partly have not been identified, namely: (tawa nggateba/kateba: local name), Kleinhovia hospita L., (tawa tamaseu: local name), (bajakah: local name), (tawa tanggedaso: local name), chlorophyll trees (spesies unidentified), (saumompai: local name), (katolanondoke: local name), Discidia albifora, and Discidia major. Conclusion: Data on endemic plants in the Kolaka and East Kolaka districts were obtained, which were used as medicinal plants and even as foodstuffs in the region and have the potential for herbal and phytopharmaca studies, so that phytochemical and pharmacological studies were needed to identify bioactive compounds.

Automatic Classification of Medicinal Plants Using State-Of-The-Art Pre-Trained Neural Networks

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity. It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges. Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%.

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

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.

Phytochemical Classification of Medicinal Plants Used in the Treatment of Kidney Disease Based on Traditional Persian Medicine.

Methods This review was focused on studying the various secondary metabolites in model plants of Iranian herbal medicine known as treatment of kidney diseases in traditional Persian medicine textbooks including Makhzan-ol-Advieh, The Canon of Medicine, and Taghvim al-Abdan fi Tadbir al-Ensan. Results Secondary metabolites of 94 medical plants belonging to 42 families were reported with their scientific and family name. Conclusion Although herbal medicines are gaining rapid popularity among people and the pharmaceutical industry, the understandings of the phytochemical and therapeutic properties of medicinal plant are important for developing effective nephroprotective medicines. Therefore, the relationship between traditional uses and biological properties should be clearly verified through further studies.

Dynamic CNN Combination for Morocco Aromatic and Medicinal Plant Classification

Dynamic CNN Combination for Morocco Aromatic and Medicinal Plant Classification

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

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.

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

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