Plant Identification Research Articles

Dhanvantri - Automated Plant Detection and Analysis in Medicine using Image Processing and Machine Learning

In recent years there has been an increasing interest in using technology to help identify and classify medicinal plants and their raw materials. In this research paper, we will use image processing techniques and machine learning algorithms to automatically identify various medicinal plants or raw materials. The goal of this approach is to use computer vision to improve the efficiency and precision of plant identification. This approach will help to facilitate botanical research and pharmaceutical manufacturing, as well as traditional medicine practice

Practical work as a means of implementing an activity-based approach to teaching biology in high school at the profile level

The article deals with the problem of forming subject competence in the application of an activity-based approach to the study of biology in high school. Therefore, the purpose of this article is to identify and substantiate specific examples of the application of the activity approach in the course of practical work in biology in the 10th grade of high school at the specialized level. It is shown that the current stage is preparatory to the implementation of the education reform in Ukraine (New Ukrainian School) in high school. Research methods: theoretical (analysis and synthesis, generalization, comparison, etc.), which were used to identify the peculiarities of using practical work in high school biology lessons; empirical (pedagogical observation) - to finalize the conclusions and prospects for further research. It is indicated that practical work in biology, provided in the school curriculum for grades 10-11 at the specialized level, provides ample opportunities for the formation of skills and abilities necessary for life in modern society. The article identifies the topics of specific practical works from the general list of them in the school curriculum in the topics "Introduction" and "Biodiversity". The possible ways and methodology of their implementation for the effective formation of subject competence in biology are shown. The implementation of these practical works involves the analytical activity of students, their identification of important components in the general array of biological information, the use of various sources and means to obtain information with the subsequent critical assessment of their reliability. It is recommended to use a number of modern mobile applications that simplify the process of plant identification - Plant Identifier & Care, Planti: Plant Care, Lovely: plants care journal, etc. The article notes that the practical work "Calculation of basic statistical indicators for comparing the variability of biological objects" is the basis for the formation of critical thinking skills and the application of the method of scientific knowledge, which is defined in the school curriculum as the expected learning outcomes. The author emphasizes the expediency of using the group method of teaching during practical work not only for the effective implementation of the activity approach, but also for the formation of students' communicative competence. The examples and methodology of performing certain practical works given in the article will help teachers to effectively use the activity approach to achieve the expected results of teaching biology to high school students at the profile level.

Design and Implementation of a Low-Cost, Linear Robotic Camera System, Targeting Greenhouse Plant Growth Monitoring

Automated greenhouse production systems frequently employ non-destructive techniques, such as computer vision-based methods, to accurately measure plant physiological properties and monitor crop growth. By utilizing an automated image acquisition and analysis system, it becomes possible to swiftly assess the growth and health of plants throughout their entire lifecycle. This valuable information can be utilized by growers, farmers, and crop researchers who are interested in self-cultivation procedures. At the same time, such a system can alleviate the burden of daily plant photography for human photographers and crop researchers, while facilitating automated plant image acquisition for crop status monitoring. Given these considerations, the aim of this study was to develop an experimental, low-cost, 1-DOF linear robotic camera system specifically designed for automated plant photography. As an initial evaluation of the proposed system, which targets future research endeavors of simplifying the process of plant growth monitoring in a small greenhouse, the experimental setup and precise plant identification and localization are demonstrated in this work through an application on lettuce plants, imaged mostly under laboratory conditions.

SMoGW-based deep CNN: Plant disease detection and classification using SMoGW-deep CNN classifier

Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.

A Review on Plant Taxonomy

The Identification and naming of plants has attracted a lot of attention throughout history. The science that investigates, identifies, describes, naming of the plant and classifies all plants. Plant taxonomy includes the description of the variation of plants, the observation of the characters like morphological, anatomical and phylogenic characters of the plants, variation, comparison and the manipulation of the data obtained from the previous information/ data is useful to arrange plants in a system of classification. The 3 aspects of the taxonomy are Identification, Nomenclature, classification are the main activates of the taxonomy. Taxonomy means law full arrangement of plants and it is a process of grouping the plants and other organisms. In ancient days plants the classification of plants was very easier and the classification of made by taking consideration of one or two morphological characters which is known as artificial classification but in course of time the classification of plants underwent many medications were taken place because of technological development. But end the end of 19th century the system of classification based on external morphological characters especially based on floral characters because floral characters are stable when compare to morphological characters while growth of the plants, this system of classification is classical/ alpha taxonomy. In course of time the classification of organisms/plants taken consideration of the multiple allied disciplines like the evidences from Anatomy, Embryology, Palynology, Cytology and Genetics. This type of classification is used to resolve taxonomic problems in addition to morphology this classification special significance which is called as biosystematics. Of course an insufficient number of experts, and the instability of phenotypes, which are easily affected by environmental factors, made this classification meet difficulties. With the advent evidences of the fields of phylogenetic, cladistics, experimental botanical gardens, field study, herbariums, keys, museums, computers and systematics, the artificial classification have progressed to a system of modern/ Phylogenic system of biological classification based on consideration of the evolutionary relationships between organisms and both biotic and abiotic factors. Here, I discussed the history of Identification, nomenclature classification and its different systems, for the identification of plants, the development of various molecular techniques that generate molecular markers has made it possible to accurately identify plants. Here I referred the reviews which gives information of the modern techniques and traditional methods that can be used for identification plant species through genotypic studies based on DNA barcoding.

Volatile chemical cues emitted by an agricultural companion plant (Cnidium monnieri) attract predatory lacewings (Chrysoperla sinica)

Plant volatiles play an important role in the recruitment of arthropod natural enemies and are widely used in foraging by many species. Therefore, identification of plants that release useful volatile organic compounds (VOCs) for influencing natural enemies and for the control of pests can be helpful in conservation biological control. Cnidium monnieri (L.) can convene many predatory enemies (lady beetles, hoverflies, lacewings) as a functional plant. The Chrysoperla sinica (Tjeder) is a dominant generalist predator, and can control the population of various pests in agricultural fields. However, how C. monnier recruits C. sinica and the chemical association between them is unknown. In this study, we tested the attractiveness of odor released by flowers and leaves of C. monnieri to C. sinica in Y-tube olfactometer. Next, we conducted solid phase microextraction (SPME), gas chromatography-mass spectrometry (GC–MS), and electrophysiological studies (EAG) to analyze the compounds and identified the volatile components which have effects on C. sinica. We also used a Y-tube olfactometer to verify the effectiveness of the volatile components on C. sinica under laboratory conditions. In olfactometer trials, C. sinica adults preferred flowers compared with leaves and fresh air. In GC–MS and electroantennography trials, seventy-six volatiles extracted from C. monnieri, twelve antenna-active components in three different concentrations (0.1 μg/mL, 1 μg/mL, 10 μg/mL) were detected for electroantennography. In the last behavioral identification assays, four synthetic compounds (10 μg/mL) had significant attractiveness of adult C. sinica antenna (i.e., γ-terpinene, trans-β-Ocimene, nerolidol, (Z)-3-Hexen-1-ol). These studies suggest that there is a chemical association between natural enemies C. sinica and the functional plant C. monnieri. This study provides a basis that can facilitate the production and application of attracting natural enemy and development and application of enhancing attractants.

Study on Real-Time Spatial Distance Acquisition Method of Transmission Line Based on Laser Point Cloud and Image Monitoring

This paper introduces the principle of safety distance diagnosis of transmission lines by using airborne LiDAR detection technology. This project intends to realize the effective segmentation of ground objects and non-ground objects through the point cloud automatic filtering technology considering the surface fluctuation characteristics. Then, this paper realizes the initial recognition of ground objects based on spatial dimension information. Then this paper extracts the ground object information based on the image data with high precision. The 3D nodes of transmission lines are obtained by Hough transform and block centroid algorithm, and the floor features are extracted by area growth algorithm. The plant feature is extracted by using plant vertex and boundary point. The distance between the power line and the surrounding objects can be measured, and the elevation of the wire and the surrounding environment can be judged accordingly. The algorithm is tested by using the observation data of unmanned airborne LiDAR, and the simulation results show that the proposed method is feasible. Through the identification of buildings, plants and other objects along the line, the harm degree of them on the power line is judged and the effective treatment is carried out. This ensures the safe operation of transmission lines.

ELMP-Net: The successive application of a randomized local transform for texture classification

This work proposes a method for texture classification based on the successive application of a local transform presented here for the first time. Such transform comprises two steps: (1) We built a two-layer mapping relating each pixel with its neighborhood, with the weights in the first layer randomly assigned; (2) We use the parameters learned by such mapping to transform the original image. Finally, we extract local descriptors at different stages of the successive application of this transform to compose the texture descriptors. The performance of our method is verified in the classification of benchmark texture databases and compared with state-of-the-art approaches. We also present an application for plant species identification. The results confirm our expectation that a model that is not based on the classical learning-based approach can still be competitive in texture analysis.

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.

The complete Chloroplast genome of Stachys geobombycis and comparative analysis with related Stachys species

Herb genomics, at the forefront of traditional Chinese medicine research, combines genomics with traditional practices, facilitating the scientific validation of ancient remedies. This integration enhances public understanding of traditional Chinese medicine’s efficacy and broadens its scope in modern healthcare. Stachys species encompass annual or perennial herbs or small shrubs, exhibiting simple petiolate or sessile leaves. Despite their wide-ranging applications across various fields, molecular data have been lacking, hindering the precise identification and taxonomic elucidation of Stachys species. To address this gap, we assembled the complete chloroplast (CP) genome of Stachys geobombycis and conducted reannotation and comparative analysis of seven additional species within the Stachys genus. The findings demonstrate that the CP genomes of these species exhibit quadripartite structures, with lengths ranging from 14,523 to 150,599 bp. Overall, the genome structure remains relatively conserved, hosting 131 annotated genes, including 87 protein coding genes, 36 tRNA genes, and 8 rRNA genes. Additionally, 78 to 98 SSRs and long repeat sequences were detected , and notably, 6 highly variable regions were identified as potential molecular markers in the CP genome through sequence alignment. Phylogenetic analysis based on Bayesian inference and maximum likelihood methods strongly supported the phylogenetic position of the genus Stachys as a member of Stachydeae tribe. Overall, this comprehensive bioinformatics study of Stachys CP genomes lays the groundwork for phylogenetic classification, plant identification, genetic engineering, evolutionary studies, and breeding research concerning medicinal plants within the Stachys genus.

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

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

Deep learning-based detection of indicator species for monitoring biodiversity in semi-natural grasslands

Deep learning (DL) has huge potential to provide valuable insights into biodiversity changes in species-rich agricultural ecosystems such as semi-natural grasslands, helping to prioritize and plan conservation efforts. However, DL has been underexplored in grassland conservation efforts, hindered by data scarcity, intricate ecosystem interactions, and limited economic incentives. Here, we developed a DL-based object-detection model to identify indicator species, a group of vascular plant species that serve as surrogates for biodiversity assessment in high nature value (HNV) grasslands. We selected indicator species Armeria maritima, Campanula patula, Cirsium oleraceum, and Daucus carota. To overcome the hurdle of limited data, we grew indicator plants under controlled greenhouse conditions, generating a sufficient dataset for DL model training. The model was initially trained on this greenhouse dataset. Then, smaller datasets derived from an experimental grassland plot and natural grasslands were added to the training to facilitate the transition from greenhouse to field conditions. Our optimized model achieved remarkable average precision (AP) on test datasets, with 98.6 AP50 on greenhouse data, 98.2 AP50 on experimental grassland data, and 96.5 AP50 on semi-natural grassland data. Our findings highlight the innovative application of greenhouse-grown specimens for the in-situ identification of plants, bolstering biodiversity monitoring in grassland ecosystems. Furthermore, the study illuminates the promising role of DL techniques in conservation programs, particularly as a monitoring tool to support result-based agri-environment schemes.

Houseplant leaf classification system based on deep learning algorithms

AbstractBotanical experts are typically relied upon to classify houseplants since even subtle differences in characteristics such as leaves can distinguish one species from another. Therefore, an automated system for recognizing houseplant leaves with accuracy and reliability becomes a valuable asset for the identification of indoor plant species. In this paper, a houseplant leaf classification system utilizing deep learning algorithms is proposed, which has been improved to effectively classify and identify a variety of houseplant leaf types. The system uses the ResNet-50 architecture based on convolutional neural network to analyze features of the leaf images and extract relevant information for classification. In addition, this work presents a newly constructed local dataset consisting of 2500 images to classify species of houseplant leaves. The dataset includes ten types of houseplant leaves that are suitable for cultivation in various climates at home. The dataset was augmented using data augmentation algorithms to increase its size and reduce overfitting. The developed system was training and testing using a local dataset. To evaluate the improved model, comparative experiments were conducted utilizing pre-trained models (original ResNet-50 and MobileNet_v2). The improved model revealed recognition accuracy of 99% with the augmented dataset and 98.60% without the augmentation, affirming its effectiveness. The improved model could potentially be used in various fields, including horticulture, plant pathology, and environmental monitoring to identify plant species.

Comprehensive Review on Plant Disease Detection and Identification

Image Agriculture is a major contributor to financial development and is the main source of income in many countries. The primary goal of disease prevention and treatment, notwithstanding the different challenges farmers face, including plant diseases, is to precisely identify and evaluate the disease while the plant is still growing. Object recognition and classification in images has been made possible by rapid advancements in deep learning, or DL, techniques. Recently, DL approaches have been applied to farming and other agricultural applications and have showed potential in a variety of areas. These plants are commonly affected by diseases such as Septoria leaf spots, bacterial spots, late plant blight, and curled yellow leaves. Our work presents a hybrid approach to early illness detection that applies region-based full convolutional networks (RFCN) for early disease identification and region-based convolutional neural networks (RCNN) for diseases classification. We construct a network model based on the EfficientNetB7 model that optimizes at several scales via dilated convolution. The network is constructed layer by layer, top-down, with several optimizations. Real-time crop images taken from crop fields are used in the experiment, and Efficient Net B7 and hybrid convolution models are used. When the suggested ensemble model is implemented in Python, the outcome demonstrates that it has high accuracy and low loss when compared to other current methodologies.

Ethno-Knowledge of Medicinal Plants to Treat Uterine Problems in Amapá Communities, Amazon, Brazil: An Integrative Review

Purpose: Review ethnobotanical studies carried out in the state of Amapá on the uses of medicinal plants for treating infection and inflammation in the uterus and subsequently identify the pharmacological effects described in the literature of these plant species Theoretical framework: In Brazil, the use of medicinal plants helps in the prevention and treatment of various diseases, and it is commonly used by women from traditional communities to treat gynecological conditions. Method: Integrative literature review. In order to search for articles, sources of information found in the following databases were used: Web of Science, Scopus, Google Scholar, LILACS and Medline. Ethnobotanical and ethnopharmacological studies carried out in Amapá from 2001 to 2021 and experimental studies (in vivo and in vitro) carried out in the period from 2018 to 2022 were included. Results and conclusion: 23 medicinal plants traditionally used for the treatment of uterine infection and inflammation in Amapá communities were identified, highlighting Fridericia chica and Stryphnodendron adstringens, which presented studies proving their antifungal efficacy against fungi of the genus Candida, mainly Candida albicans. The study helps to recover popular knowledge about medicinal plants used alternatively for the treatment of gynecological conditions Research implications: This study contributes to the identification of plant species traditionally used in gynecological pathologies and identifies the pharmacological effects of these vegetables in the literature, serving as a support for biotechnological development and the discovery of new drugs. Originality/value: This review sought to present to the scientific and pharmaceutical community what is currently being researched on bioactive compounds from medicinal herbs aimed at treating infection and inflammation that affect women's gynecological health.

Identification of biotic and abiotic factors coal mine overburden on Warukin rock formation of South Kalimantan

Overburden (the dumping of mine tailings and other reject materials) from the Warukin geological formation is one of the potential acid-forming (PAF) of 33.17%. Mining can cause overburden to be exposed and affect biotic and abiotic conditions. The study aimed to analyze the biotic and abiotic factors of three different age overburdens from the Warukin Formation, South Kalimantan. Biotic factor measurements included vegetation sampling using line transect, soil insects using pitfall traps, and microbial using total plate count. Abiotic factor measurements included air temperature and humidity, light intensity, temperature, moisture, and pH of the substrate (n=3), and chemical overburden. Identification of plants and insect species using information from locals, identification books, and websites. Plant species diversity was analyzed using the Shannon-Wiener Index. Dominance species were analyzed using the Important Value Index (IVI). The relation of overburden age on biotic and abiotic factors was analyzed multivariate with Non-Metric Multidimensional Scaling (NMDS). The results showed that there are 102 species of plants and 11 species of soil insects. The numbers of fungi and bacteria are 10-4 CFU g-1 and 10-3 CFU g-1, respectively. Plant species diversity is classified as medium (H'= 2.34; 2.50 and 2.82). Changes in plant composition occurred as the microclimate and substrate nutrition improved. The organic matter increased over time. Humidity and light intensity influence the presence and composition of organisms, while pH values affect the adaptability of plants, insects, and microbes. Heavy metal concentrations have increased due to the physicochemical properties of the Warukin formation overburden.

Incorporation of standardised extract of Curcuma longa Linn into phytosomes and its evaluation for in vitro Anti-inflammatory potential and Brine shrimp lethality assay

Aim: To incorporate standardized Curcuma longa Linn extract into phytosomes and evaluate for in vitro anti-inflammatory and BSL bioassay. Method: The quality of the plant material was determined by various pharmacognostic parameters. The plant material was then subjected to maceration for extraction using ethanol: water as solvent followed by Soxhlet extraction. The resulting extract was subjected to phytochemical analysis to determine the presence of plant metabolites. The drug and excipients compatibility was evaluated by FTIR study. Furthermore, using the thin film hydration approach, a new lipid-based phytosome was prepared. In vitro anti-inflammatory and brine shrimp lethality tests were performed on prepared phytosome. Results: Moisture content, total ash, acid insoluble ash, water soluble ash values, aqueous, alcohol, and petroleum ether extractive values are all found to be within limits. The phytochemical analysis validated the existence of alkaloids, tannins, resins, carbohydrates, proteins, flavonoids, and saponins. The compatibility study demonstrates the compatibility of excipients with drugs. Thin film hydration technique was employed successfully to prepare the phytosomes containing Curcuma longa linn extract. In vitro anti-inflammatory activity revealed that prepared phytosome could serve as natural based therapeutic option for anti-inflammatory potential. Brine shrimp lethality assay also confirmed the bioactivity of prepared phytosomes. Conclusion: The method used for standardization can be used to aid with plant identification and quality analysis of Curcuma longa Linn for future research. It can be inferred from the findings that phytosomes loaded with Curcuma longa Linn extract exhibited promising anti-inflammatory and cytotoxic effects.

Use of a mobile plant identification application and the out-of-school learning method in biodiversity education.

Today, many students are no longer able to identify plants and researchers use the term "plant blindness" to describe students' ignorance of plant species. Knowledge of plant species is among the factors that best support an interest in and understanding of environmental issues, biodiversity, and a sustainable lifestyle. With the help of mobile technologies, it is thought that the knowledge level of students about herb and tree varieties can be increased outside of class hours and in outdoor education. The aim of this study was to determine the effect of the use of the PlantNet mobile application and the out-of-school learning method on the knowledge levels of 5th-grade students about the plant species in their environment and their behaviors demonstrating an understanding of biodiversity. For this purpose, at the beginning of the study, a plant species questionnaire and a biodiversity behavior questionnaire were applied to the students. Afterward, the students were asked to examine the plant species around them using the PlantNet mobile application. At the end of the activities, the data collection tools applied at the beginning of the study were applied again. It was determined that the students who participated in the activities displayed more biodiversity-related behaviors than before they participated in the activities, and that the students were able to write down more herb and tree species at the end of the activities. In addition, as a result of the activities, a positive and high-level relationship was found between the students' biodiversity-related behaviors and the total number of plant species they knew.

Melissopalynological Analysis of Honey from French Guiana.

Beekeeping directly depends on the floral biodiversity available to honey bees. In tropical regions, where nectar and pollen resources are numerous, the botanical origin of some honey is still under discussion. A precise knowledge of plants foraged by honey bees is useful to understand and certify the botanical origin of honey. In this study, attention was paid to honey samples from the French Guiana Atlantic coast where beekeepers generally place their hives in four types of biotopes: seaside vegetation, mangrove, savannah, and secondary forest. Pollen analysis of 87 honey samples enabled the identification of major plants visited by Africanized honey bees during the dry season (approximately from July to January). Through melissopalynologic analysis, 51 pollen types were identified and classified according to their relative presence. Frequently observed pollens (with relative presence > 50%) in French Guiana kinds of honey were those from Mimosa pudica, Cocos sp., Rhyncospora sp., Avicennia germinans, Paspalum sp., Spermacoce verticillata, Tapirira guianensis, Cecropia sp., Myrtaceae sp., Mauritia flexuosa sp., Solanum sp., and Protium sp. In many honeys, only M. pudica was over-represented (relative frequency > 90%). Color and electrical conductivity in French Guiana honeys exhibit significant variations, with color ranging from 27 mm to 110 mm Pfund, and electrical conductivity ranging from 0.35 to 1.22 mS/cm.