Identification Of Medicinal Plants Research Articles

Verified localization and pharmacognosy of herbal medicinal plants in a combined network framework

Pharmacognosy from medicinal plants involves the scientific domain of medicinal compounding based on their medicinal properties. Accurate identification of medicinal plants is crucial, especially by examining their leaves. Choosing the wrong plant species for medicinal preparations can have adverse side effects. This study presents a Human-Centered Artificial Intelligence approach for medicinal plant identification, combining a YOLOv7-based Leaf Localizer with a leaf Class Verifier based on DenseNet through a Confidence Score Analyser algorithm. The Confidence Score Analyser ensures reliability by evaluating predicted categories against predefined thresholds, and the ensemble technique through majority voting enhances robustness. An average performance gain of 25.66% sensitivity is observed when comparing the YOLO object detection model with 77.45% precision to the YOLO integrated with the class verifier model with 97.33% precision. Consistent sensitivities are achieved through the ensemble technique, showcasing robustness across diverse scenarios. The final step incorporates automated textual and audio pharmacognosy information about the identified medicinal plant properties and their utility. Real-time applicability as a smart phone application makes this approach invaluable for medicinal plant collectors and experts.

Automated Medicinal Plant Identification and Use Case Generation: A Convolutional Neural Network-Xception Approach

Abstract: In the realm of botany, pharmacology, and herbal medicine, the identification of medicinal plants stands as a crucial yet labor-intensive endeavor. This project presents a pioneering approach to automate plant identification through the utilization of advanced deep learning techniques, specifically employing the Xception architecture within a Convolutional Neural Network (CNN). The methodology involves the acquisition of plant images, followed by preprocessing and dimension reduction to 299x299 pixels. Subsequently, the model undergoes rigorous training, achieving commendable accuracies of approximately 85% in training, 93% in validation, and 94% in testing phases. The trained model is then deployed through a user-friendly web interface, allowing seamless interaction for uploading images and receiving comprehensive plant classification results. Leveraging Flask for backend integration, this system bridges the gap between cutting-edge technology and botanical expertise, enabling researchers, botanists, pharmacologists, and herbalists to expedite plant identification processes with heightened accuracy and efficiency. Furthermore, this innovation not only streamlines identification tasks but also provides detailed insights into the diverse applications and attributes of medicinal plants, thereby fostering advancements in research, conservation, and utilization for the betterment of human health and well-being..

Medicinal Plant Identification using Machine Learning

Medicinal plants play a crucial role in traditional and modern medicine due to their properties. In this study, we propose the identification of medicinal plants using machine learning. The proposed system comprises several key components: data collection, feature extraction, model training, and prediction. Initially, a comprehensive dataset of medicinal plant images is gathered from databases. Next, relevant features such as leaf shape, texture, and colour are extracted from images using image processing techniques. Subsequently, ML algorithms such as Convolutional Neural Networks (CNNs) are employed to train a classification model on the extracted features. The trained model is then capable of accurately identifying medicinal plants from input images. To facilitate user interaction, a user-friendly interface may be developed, allowing users to upload images and receive instant identification results. Furthermore, the system's performance is evaluated in real-world scenarios to assess its practical utility and reliability. Overall, the proposed automated medicinal plant identification system represents a significant advancement in leveraging ML technology to streamline the identification process

Medicinal Plant Identification using Machine Learning Algorithm

The identification of medicinal plants plays a crucial role in various fields including pharmacology, traditional medicine, and biodiversity conservation. Traditional methods of plant identification are often time-consuming, labor-intensive, and require expertise in botanical taxonomy. In recent years, the application of Convolutional Neural Networks (CNNs) has shown promising results in automating the process of plant species recognition. This paper provides a comprehensive review of recent advances in medicinal plant identification using CNNs. This study discuss the methodology, challenges, and opportunities associated with CNN-based approaches, as well as their potential applications in pharmacological research and healthcare. Furthermore, we highlight key datasets, architectures, and performance metrics used in CNN-based plant identification systems. Finally, we identify future research directions and potential areas for improvement in this rapidly evolving field

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

Checklist of medicinal plants used by traditional women for maternal health care in Lagos State, Nigeria

Traditional women in Lagos State, Southwest, Nigeria have been using medicinal plants for maternal health care since time immemorial. However, there is no proper documentation of medicinal plants used in maternal health care, as information is passed from generation to generation orally. This study aimed at the identification and documentation of medicinal plants used in Lagos, Nigeria for maternal health care. Ethnobotanical studies were carried out in six local government areas namely, Agege, Alimosho, Badagry, Ejigbo, Mushin and Ojo. A Semi-structured questionnaire was designed to obtain information from 300 respondents, 50 from each local government area. The respondents included herbal merchants, herbal medicine practitioners, midwives, and traditional birth attendants. Information obtained included the names of the plants, plant parts used, mode of preparation and administration and dosage of the medicinal herbs. Forty-five (45) plants belonging to twenty-three (23) families were identified and documented in the study area. Most of the reported plants belong to the Fabaceae (13.52%), Malvaceae (8.41%) and Annonaceae (6.67%) families. Kigelia africana, Caliandra portoricensis, Nauclea latifolia and Securidata longepedunculata had the highest Relative Frequency of Citation (RFC) and Fidelity Level (FL) of 0.96; 100% and 0.82; 100%, 0.92; 100 % and 0.78; 100% respectively. Most of the reported plants (73.47%) were herbs and shrubs and about 62.68% of the surveyed plants were wild. Leaves were the most frequently used (72.74%) plant’s part. Most of the herbs (80.45 %) were prepared as decoctions and preparations were mostly administered orally (88.64%). Strategies suggested to conserve the surveyed plants include the cultivation of medicinal plants to prevent their extinction.

Ethnobotanical Study of Medicinal plants used for Traditional Diarrhea Treatment in North-East Algeria

The objective of this study is to aid in the identification and documentation of medicinal plants that are utilized in traditional local pharmacopeia for the treatment of diarrhea in four provinces located in the northeast region of Algeria. The ethnobotanical survey was conducted during 2019-2020. All users of medicinal plants in those regions were interviewed using a guide questionnaire. Stratified sampling was used to collect information on medicinal plants. Furthermore, the study established the relative importance of the species and estimated the informant consensus factor (ICF) for the medicinal plants included in research. The study has found a total of 52 species that are commonly employed in the treatment of diarrhea in four specific provinces of Algeria. These species are categorized into 26 families and 22 genera. The most frequently quoted plants (over 10 quotations) were Artemisia herba alba Asso. (52 quotations) with the highest (FC = 43.33%); Punica granatum L. (50 quotations, FC = 41.67%); Origanum vulgaire L. (32 quotations, FC = 26.67%); Juniperus phoenicea L. (29 quotations, FC = 24.17%), and Ceratonia siliqua L. (19 quotations, FC = 15.83%). The most common plant families were Lamiaceae and Rosaceae (10 and 6 plants, respectively). Species morphological type analyses show that herbs were most represented (46.16%). For different organs used, leaves and fruits were the most used (27.15% and 26.35%, respectively). Although the infusion (55.81%) constituted the prevailing approach to drug preparation, the results showed that many kinds of plants can be used to treat diarrhea in Algeria. The study has enhanced the collection of medicinal plants with anti-diarrheal properties and has contributed to the advancement of knowledge in traditional medicine while also preserving local indigenous knowledge.

An Inventory of Medicinal and Poisonous Plants of the National Veterinary Research Institute Vom, Plateau State, Nigeria

Traditional medicinal plants are still commonly used for primary healthcare in developing countries, and this study seeks to identify the types of medicinal plants present in our community and document the knowledge of poisonous plants. The study area was divided into four blocks, and the plants within each block were identified through a field survey using Android phones equipped with Google lens and GPS. Local and scientific names of the medicinal plants were identified using photographs, herbaria, and references. The study found a total of 98 medicinal plant species belonging to 41 plant families, with Fabaceae, Euphorbiaceae, and Lamiaceae families having the highest number of species. This study is significant for conserving traditional medicinal plants and adding to the inventory of medicinal plants in the area for conservation purposes. Furthermore, it is essential for public health and safety, as proper identification of medicinal plants is crucial to avoid poisoning

Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (PI) and a blend of public and field data (PFI), the latter featuring intricate backgrounds. Our study elucidates the robustness of these models in accurately identifying and classifying both interfamily and interspecies variations. Despite variations in accuracy across diverse families and species, the models demonstrated adeptness in these classifications. Comparing the models, we unearthed a crucial insight: the Normalized leverage factor (γω) for DenseNet201 stands at 0.19, elevating it to the pinnacle position for PI with a remarkable 99.64 % accuracy and 98.31 % precision. In the PFI scenario, the same model achieves a γω of 0.15 with a commendable 97 % accuracy. These findings serve as a guiding beacon for shaping future application tools designed to automate medicinal plant identification at the user level.

DNA barcoding application for potential medicinal plants on Ziziphus species from Aketajawe-Lolobata National Park, North Moluccas

Aketajawe-Lolobata National Park is a protected national park located in the province of North Moluccas, Indonesia. The national park has been known to be rich in biodiversity with the existence of many potential medicinal plants such as Ziziphus. DNA barcoding can be a useful tool for identifying and characterizing these plants for potential medical applications. Research and exploration have been conducted in Aketajawe-Lolobata National Park in November and December 2022. This study aimed to analyze how the DNA barcoding regions aid identification for potential medicinal plants. The genomic DNA were amplified by matK primers, and the sequences showed high similarity rate with their homologs, then the phylogenetic tree was constructed using the Neighbor-Joining method to assess their cross-species relationship. The phylogenetic study showed that the species that have been identified based on morphological characters, i.e., Ziziphus angustifolia and Z. mauritiana were clustered with Z. angustifolia and Z. mauritiana retrieved from GenBank. However, the unidentified species are closely related with Z. calophylla obtained from GenBank but with low bootstrap support. All the results indicate that DNA barcoding approach could be successfully used for reliable identification of medicinal plants and matK gene is a good candidate for this approach.

Remote Sensing Application in Chinese Medicinal Plant Identification and Acreage Estimation—A Review

Chinese Materia Medica Resources (CMMRs) are crucial for developing the tradition and industry of traditional Chinese medicine. Given the increasing demand for CMMRs, an accurate and effective understanding of CMMRs is urgently needed. Chinese medicinal plants (CMPs) are the most important sources of CMMRs. Traditional methods of investigating medicinal plant resources have limitations, including severe subjectivity and poor timeliness, which make it difficult to meet the demand for real-time monitoring of large-scale medicinal plant resources. In recent years, remote sensing technology has become an important means of obtaining information on medicinal plants, and the application of this technology has made up for the shortcomings of traditional methods. This paper first discusses the development of investigation methods of CMMRs; points out the importance of remote sensing technology in the application of spatial distribution and information identification and extraction of Chinese medicinal plant resources (CMPRs); analyzes the characteristics of CMPs in different planting patterns, different habitats, and different regions from the perspective of remote sensing information extraction; and explores the selection of suitable data sources, providing a reference for medicinal plant identification and information extraction. Secondly, according to the existing classification and identification methods, previous studies are summarized from the perspectives of classification scales, classification features, and classification accuracy, and the advantages and disadvantages of the commonly used remote sensing classification methods in the investigation of CMPRs are summarized and compared. Finally, the development trend of remote sensing technology in the identification and information extraction of CMPs is examined, and the key technical problems to be solved in the identification and classification of CMPs and the extraction of area information are summarized so as to provide technical support and experience references for the application of remote sensing in the investigation of CMPRs.

Developing a Neural Network-based Model for Identifying Medicinal Plant Leaves using Image Recognition Techniques

Herbal plants play a vital role in human health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. There has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques to address this. The proper identification of plant methods using computer vision and neural network techniques has been proposed. This approach involves neural network models such as CNN, ALexnet and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve species identification techniques, and protect endangered species.

Developing A Neural Network-Based Model for Identifying Medicinal Plant Leaves Using Image Recognition Techniques

Herbal plants contribute an important role in people's health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. To address this, there has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques. The exact recognition of plants uses methodologies like computer vision and neural networks, which have been proposed earlier. This approach involves neural network models such as CNN, ALexnet, and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve techniques for species recognition, and participate in the preservation of species that are considered ad endangered.

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

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.

Identification of Medicinal Plants Used by the Community of Watumite Village, Nangapanda District, Ende Regency as a Biology Learning Media

The aim of the research is to find out the types of medicinal plants used by the Watumite Village community, find out how to use each type of medicinal plant used and find out what medicinal plant booklets are used by the Watumite Village community. Watumite Village community is suitable for use as a biology learning medium. The type of research used is research and development with the ADDIE model (analysis, design, development, implementation, evaluation). This research uses a qualitative and quantitative descriptive approach. The qualitative approach used is to describe the types of medicinal plants used by the Watumite Village community. Meanwhile, a quantitative approach was carried out by calculating the results of the validation of the booklet developed which was then analyzed descriptively. The results of research on medicinal plant identification based on interviews with 10 respondents living in 4 hamlets in Watumite Village obtained 32 types of medicinal plants originating from 22 families. The findings of 32 types of medicinal plants packaged as Biology learning media in booklet form have been validated by material experts and media experts with a percentage value of 85% from material experts and 84% from media experts, declared valid. The conclusions from this research are: (1) There are 32 types of medicinal plants used by the community in Watumite Village; (2) The use of each type of medicinal plant includes the part of the plant used as medicine, the type of disease treated, and the method of processing; (3) Development of a Medicinal Plants booklet for the Watumite Village Community is categorized as suitable as a biology learning medium.

CNN-based medicinal plant identification and classification using optimized SVM

CNN-based medicinal plant identification and classification using optimized SVM

ANATOMY OF THE ENDEMIC ALYSSUM PATERI SUBSP. PATERI

Objective: Alyssum pateri Nyár. subsp. pateri is an endemic plant known as "kanatlı kevke" in Türkiye. The plant is perennial and in semi-shrub form. The plant is traditionally used externally in the treatment of rheumatism in Bingöl (Türkiye). Microscopic analysis of the anatomical structures of plants can provide useful information for taxonomic classification. Light microscopy analysis is a widely used and effective method for the identification of medicinal plants. In this study, the anatomical features of the A. pateri subsp. pateri were examined. Material and Method: The plant material was collected from Ankara (Türkiye). The samples were protected in 70% alcohol. The cross and surface sections were cut by hand with a razor blade into microscopic preparation form. A Leica DM 4000B microscope was used for anatomical analysis and micro photographs. Result and Discussion: The anatomical characters of the leaf, petiole and stem of the Alyssum pateri subsp. pateri were revealed. The leaf is dorsiventral and contains 1-2 rows of palisade parenchyma. Cruciferous stomata and stellate hairs are located on the lower epidermis. The petiole cross-section is sulcate and stellate hairs are observed in the epidermal layer. The main vein is arc-shaped and is accompanied by lateral veins. The stem is disc-shaped. Stem epidermis contains stellate hairs. The interfascicular tissue between the xylem strands of the vascular bundles is composed lignified cells. Vascular bundles are surrounded by a pericyclic sclerenchymatous cap.

Identification of medicinal plant using hybrid transfer learning technique

Ayurveda is one of the oldest holistic treatment systems in the world. Finding an accurate and correct plant is the key to the working of Ayurvedic treatment. Identification of medicinal plants is a tedious job due to the look-alike feature and availability issue of medicinal plants with other plants. This paper emphases on the ideal identification and classification of plants of medicinal use using deep learning approaches. Previously researchers have used traditional machine learning techniques to identify medicinal plants, which lead to mixed results. Such results are good but not enough as the identification of medicinal plants may lead to a worsening situation for patients. This research is conducted to get results closer to an ayurvedic expert. The dataset used for this research has been taken from Mendeley Data. The dataset comprises 30 different species of medicinal plants. Hybrid transfer learning has been applied to this dataset. The model has generated a test accuracy of 95.25% which is better than the other popular transfer learning techniques.

Automated System for Medicinal Plant Classification Usage Recommendation

Abstract: We propose the utilization of deep learning to create an automated system for classifying medicinal plants, enabling swift identification of beneficial plant species. Convolutional neural networks (CNNs) are renowned for their exceptional capabilities in extracting and classifying features. Our proposed model, named "deep learning model," is developed by extracting features and employing a CNN classifier, resulting in high accuracy and minimal prediction time when applied to real-time images. The system consists of three modules: image pre-processing, image feature extraction, and image classification. During the initial pre-processing step, we conduct RGB conversion to extract the green band from input images. In the subsequent step, after pre-processing, we extract features such as shape, color, and texture from the pre-processed image. These features are then utilized to classify the image as either medicinal or a regular leaf. Plants have been utilized as a medicinal resource in Ayurveda since ancient times. The identification of the correct plant is a crucial step in the preparation of Ayurvedic medicine, traditionally carried out manually. However, with the need for mass production, it becomes essential to automate the process of plant identification. In this study, we propose the implementation of a technique that employs a CNN algorithm, along with an ensemble supervised machine learning algorithm that considers color, texture, and geometrical features, for the identification of medicinal plants. The Convolutional Neural Network (CNN) is utilized for classification and determining the plant class