Photographs Of Plants Research Articles

EHerbarium: A Global Platform for Exploring and Sharing Herbarium and Live Plant Images to Enhance Biodiversity Preservation

Historically, herbaria have been instrumental in preserving and studying plant biodiversity. The widespread use of smartphones has led to a surge in the digital documentation of plants. Over the last decade, herbarium digitizing projects are also underway. Many of the digitized herbarium images are available online through different portals. The examples of these portals include Kew Gardens and New York Botanic Garden Herbarium. Combining the latest mobile, cloud and AI technologies as well as plant taxonomy and herbarium artifacts, the mission of eHerbarium*1 is to provide an accessible digital platform for plant enthusiasts, including botanists, researchers, students, as well as the general public around the world, to collect, identify and share live plant and herbarium images. It promotes data collection and data sharing around the globe for biodiversity preservation. It further provides educational benefits about plants and the plant biodiversity around us and the world. eHerbarium offers access to an extensive collection of herbarium images from Kew’s Herbarium at the Royal Botanic Gardens, Kew. The platform is also extensible, allowing it to host images from other herbaria. In addition, this platform provides high-quality botanical visuals, enabling users to capture and share live plant photos or herbarium images, or upload from existing collections. Customizable licensing options facilitate community use and enhance collaborative botanical research. The platform’s AI-powered tools streamline plant identification, offering autocomplete suggestions for taxonomy details such as scientific name, family name or common names. Our proprietary technology ensures seamless and accurate botanical data entry by automatically retrieving corresponding scientific or common names. Additionally, eHerbarium enriches botanical records by automatically associating image locations with Biodiversity Information Standards (TDWG) Level 2 regions. TDWG Level 2 regions are specific geographical areas defined by TDWG to standardize the recording and reporting of biodiversity data. These regions, which typically correspond to large countries or significant subnational areas, help ensure consistent and accurate geographical referencing in botanical studies and herbarium records, while also respecting image owners' location privacy. eHerbarium has more than 40,000 worldwide downloads since its debut less than six months ago on March 18, 2024. We encourage further collaborations to expand our repositories for herbarium and live plant images. We also welcome partnerships to tailor the application to specific needs for natural history and biodiversity data collection.

Contribution to Egypt’s Flora: new records from Nile Islands, South Valley, Egypt

The rate at which people are introducing species to formerly uninhabited places shows no signs of slowing down. Wild plants can roam freely across international borders. From September 2021 to June 2023, 45 field excursions were made to Egypt’s southern Nile Islands in the Aswan Governorate to undertake vegetation surveys. Depending on the literature reviewed and herbaria consulted during this study, three plant taxa, as well as one additional genus (Macroptilium), found during these field excursions were not previously recorded in the Egyptian Flora. These new records are Acanthospermum hispidum (Asteraceae), Geranium carolinianum (Geraniaceae), and Macroptilium atropurpureum (Fabaceae). These plants are classed as noxious weeds that harm agriculture. A complete description, occurrence and distribuion maps, plant photos and illustrations are provided. This study underlines the importance of carrying out extensive and ongoing research on the flora of Aswan’s Nile Islands to have a better knowledge of flowering plant distribution patterns. In addition, it might be regarded as a great starting point for expanding floristic knowledge of this interesting territory and its surroundings.

From simple labels to semantic image segmentation: leveraging citizen science plant photographs for tree species mapping in drone imagery

Abstract. Knowledge of plant species distributions is essential for various application fields, such as nature conservation, agriculture, and forestry. Remote sensing data, especially high-resolution orthoimages from unoccupied aerial vehicles (UAVs), paired with novel pattern-recognition methods, such as convolutional neural networks (CNNs), enable accurate mapping (segmentation) of plant species. Training transferable pattern-recognition models for species segmentation across diverse landscapes and data characteristics typically requires extensive training data. Training data are usually derived from labor-intensive field surveys or visual interpretation of remote sensing images. Alternatively, pattern-recognition models could be trained more efficiently with plant photos and labels from citizen science platforms, which include millions of crowd-sourced smartphone photos and the corresponding species labels. However, these pairs of citizen-science-based photographs and simple species labels (one label for the entire image) cannot be used directly for training state-of-the-art segmentation models used for UAV image analysis, which require per-pixel labels for training (also called masks). Here, we overcome the limitation of simple labels of citizen science plant observations with a two-step approach. In the first step, we train CNN-based image classification models using the simple labels and apply them in a moving-window approach over UAV orthoimagery to create segmentation masks. In the second phase, these segmentation masks are used to train state-of-the-art CNN-based image segmentation models with an encoder–decoder structure. We tested the approach on UAV orthoimages acquired in summer and autumn at a test site comprising 10 temperate deciduous tree species in varying mixtures. Several tree species could be mapped with surprising accuracy (mean F1 score =0.47). In homogenous species assemblages, the accuracy increased considerably (mean F1 score =0.55). The results indicate that several tree species can be mapped without generating new training data and by only using preexisting knowledge from citizen science. Moreover, our analysis revealed that the variability in citizen science photographs, with respect to acquisition data and context, facilitates the generation of models that are transferable through the vegetation season. Thus, citizen science data may greatly advance our capacity to monitor hundreds of plant species and, thus, Earth's biodiversity across space and time.

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

An Artificial Intelligence Framework for Disease Detection in Potato Plants

Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively.

An Evaluation of Various Machine Learning Approaches for Detecting Leaf Diseases in Agriculture

Background: Machine learning has shown remarkable promise in recent years for use in areas such as pattern detection and categorization. The diagnosis of diseases is crucial in agriculture since they are a natural occurrence in plants. The easiest and most effective way to identify crop disease is through the use of image processing, computer vision and machine learning techniques. Methods: To identify and categorize cotton leaf diseases, the study compares the effectiveness of established techniques like Support Vector Machine (SVM) and random forest with state-of-the-art techniques like neural network (CNN) methods and architectures like Inceptionv3, VGG16 and RasNet50 with data augmentation and transfer learning. Result: The models were trained with four distinct types of plant photos that were manually gathered from a government agency and a farm. It was also noted that as the quantity of training data rose, so performed the resultant models.

Pinellia discolor: a new cryptic species distinguished from P. cordata in mainland China

Pinellia cordata (Araceae) is a perennial herb species distributed in China and Japan. Several studies have shown that there are two types of this species, and chromosome counts have shown irregular variation among populations. In this study, we investigated the morphological characteristics, established the chromosome number in root tips, reconstructed phylogenetic relationship based on whole chloroplast genomes, and elucidated biogeographic patterns based on data collected from the Chinese Virtual Herbarium (CVH) and the Plant Photo Bank of China (PPBC) for the two types of P. cordata, including type I (greenish leaf blade) and type II (colored leaf blade). We found stable significant differences in morphometric characteristics (color of the leaf blade abaxially, petiole, spathe and appendix) and the number of chromosomes. The results of the phylogenetic analysis showed that types I and II were located to different places in the evolutionary tree compared to the group. The distribution regions of these types of P. cordata were also slightly different in mainland China. These findings suggested that P. cordata should be divided into P. cordata (type I) and Pinellia discolor (type II, a cryptic species) and the latter is here described as a new species.

PLANT DISEASE DETECTION BY USING MOBILENTV2 AND XCEPTION ON FILTERED AND ENHANCED IMAGES

The gathering, sorting, and processing of plant leaf images serves as the foundation for this study. These are crucial first steps in the plant health monitoring process that guarantee reliable findings. The work classifies and detects plant leaf photos, extracting data on plant health using state-of-the-art deep learning models like Xception and MobileNetV2. In order to assess the effectiveness of the system, additional filters are applied to the photos of plant leaves in order to adjust characteristics like brightness, contrast, sharpness, and blur. The study's results show that the deep learning models employed could accurately determine the health of plant leaves, offering important new information for related future research.

Precision Agriculture through Deep Learning Algorithms for Accurate Diagnosis and Continuous Monitoring of Plant Diseases

For sustainable food production, precision agriculture is essential, and one of its main tenets is the precise identification and ongoing surveillance of plant diseases. Conventional approaches to illness monitoring and detection are frequently labour-intensive, time-consuming, and dependent on visual inspection, which increases the risk of misidentifying diseases. Deep learning algorithms have surfaced as a potentially effective way to tackle these issues. In this study, we introduce a novel method for precision agriculture that improves plant disease diagnostic accuracy and offers continuous monitoring by utilising deep learning algorithms. Our research uses cutting-edge convolutional neural networks (CNNs) and ResNet50 to precisely identify illness symptoms in plant photos. The proposed deep learning model is trained on an extensive dataset of plant photos illustrating a range of illnesses, enabling it to identify minute visual cues that human observers might overlook. Compared to previous ML methods, the model's accuracy in detecting diseases is higher, which lowers the possibility of misdiagnosis and facilitates early intervention to minimise crop damage. By placing cameras and sensors in the fields, proposed system provides continuous monitoring in addition to precise diagnosis. The proposed deep learning model processes the real-time data and photos of the crops that are captured by these devices.

Feasibility Study of Planting and Developing Atlas Plants (Petunia hybrida L.) and Chrysanthemums (Chrysanthemum morifolium L.) in Urban Streets of Karaj Considering the Effects of Light Pollution

Light pollution is the effect of inefficient and unnecessary artificial lighting in outdoor spaces. Each plant requires a certain amount of light for its life and photosynthesis processes to continue. Petunia, scientifically known as Petunia hybrida L., is an annual plant belonging to the Solanaceae family and is a short-day plant photo periodically. Chrysanthemum, scientifically known as Chrysanthemum morifolium L., belongs to the Asteraceae family and is a long-day plant photo periodically. These two plant species, Petunia and Chrysanthemum, which are planted in urban green spaces, were selected for the current study to determine the effects of nighttime light radiation. The present research was conducted during the year 1041 in the research greenhouse of Kharazmi University, Karaj campus, to investigate the effect of light pollution with 3 treatments: control, 44 lux, and 1344 lux, each replicated 3 times. Then, the results of morphological and physiological traits were examined. In Petunia, the results of the analysis of variance showed that most morphological traits of the light pollution treatment significantly differed from the control treatment, while no significant effect was observed for physiological traits. The 44-lux treatment had the most significant effect on plant height, root length, wet and dry stem weight, leaf surface area, chlorophyll a, chlorophyll b, total chlorophyll, and flavonoid. In Chrysanthemum, the analysis of variance results indicated that light pollution had a significant effect on some morphological traits. Overall, in this plant, stem and root length decreased compared to the control plant when light pollution was introduced. Overall, it can be concluded from the analysis of the data obtained from the experiment with a minimum of 3 replications using Duncan's multiple range test based on a completely randomized design that light pollution had a positive effect on Petunia (a long-day plant) at 44 lux. However, no specific effect was observed in Chrysanthemum (a short-day plant).

Primula kawasimae H. Hara (Primulaceae) on the Kamchatka Peninsula

Primula kawasimae H. Hara is reliably known in Russia only on Sakhalin Island and Kamchatka Peninsula. It is listed in the Red Books of Sakhalin Region and Kamchatka Krai. For a long time it was listed for Kamchatka based on the only collection of V. V. Yakubov from the eastern coast of the peninsula. In 2017, the species was found in Central Kamchatka (Bystrinsky District). In 2022, habitats with abundant Primula kawashimae were found in the Bystrinsky District. We took photos of plants in different phenological phases, collected herbarium specimens and seeds, and determined chromosome numbers.

Expositionen mit Fruchtpflanzen in Deutschland im Zeitraum 2010–2019

ZusammenfassungHintergrundAnfragen zu Fruchtpflanzen sind ein häufiger Konsultationsgrund von Giftinformationszentren, wobei hervorzuheben ist, dass es keine großen systematischen Studien zur Giftigkeit auf Grundlage von Expositionsdaten gibt. Ziel der Arbeit ist die Bestimmung des Vergiftungsrisikos durch Fruchtpflanzen in Deutschland.MethodenRetrospektive Untersuchung der Daten des Gemeinsamen Giftinformationszentrums Erfurt zu Vergiftungsanfragen bzgl. Fruchtpflanzen (2010–2019) mit ausführlicher Darstellung vorab publizierter Zwischenergebnisse, tabellarischer Handreichung und Pflanzenfotografien als Identifizierungshilfe sowie Trendanalysen.ErgebnisseAus 16.088 Pflanzenexpositionen mit 16.700 Pflanzen wurden 214 verschiedene Fruchtpflanzenarten identifiziert. 45 Fruchtpflanzenarten (21 %) stellten sich als relevant (≥ 30 Anfragen) heraus, davon 6 (2,8 %) als hochrelevant (≥ 300 Anfragen). Allen relevanten Pflanzen wurde eine definierte Risikokategorie (RK) zugeordnet: RK 0 (2; 4,4 %), RK 1 (26; 57,8 %), RK 2 (12; 26,7 %) und RK 3 (5; 11,1 %). Von den Anfragen bezogen sich 6 % (459/7607) auf RK 0, 47,9 % (3645/7607) auf RK 1, 39,3 % auf RK 2 (2986/7607) und 6,8 % (517/7607) auf RK 3. 69,5 % (5284/7607) der Anfragen betrafen Kleinkinder (1 bis < 6 Jahre). Die Expositionsfolgen waren für alle Altersklassen zu 82 % symptomlos, 14,7 % leichtgradig, 3 % mittelschwer und 0,3 % schwer, wobei schwere Vergiftungen durch 7 Pflanzenarten verursacht wurden. Eine Intervention wurde in 66,8 % (5079) der Anfragen eingeleitet. Anfragen betrafen am häufigsten: Taxus baccata, Ligustrum vulgare, Physalis alkekengi, Prunus laurocerasus, Convallaria majalis, Mahonia spec., Sambucus spec., Lonicera spec., Sorbus aucuparia, Thuja spec., Hedera helix und Cotoneaster spec.DiskussionVergiftungen durch Fruchtpflanzen in Deutschland sind selten. Es besteht allerdings ein hoher Informations- und Aufklärungsbedarf.

Forecasting diseases that affect plant leaves and moisture levels in the soil using a data mining approach

The foundation of the global and Indian economies is agriculture. Since agriculture started millions of years ago, many environments, civilizations, and technical developments have fostered and defined the evolution of agricultural technology. In this study, we examine how we may analyze images of plants and soil to better keep tabs on their health, as well as how we can determine how much water each kind of plant needs. Images of the plants and soil are first taken using a digital camera with the necessary resolution. The form and geometric characteristics are extracted from the plant images using the inner distance shape context-based descriptor and geometrical descriptors. The soil images are also used to extract features and color properties. The botanical plant species dictionary is used to identify the plant type using the contour elements of the plant photos. Gradient structured random forest (GS-RF) classification is used to forecast leaf diseases. Principal Component Analysis (PCA) and Hierarchical Gradient Deep Neural Network (HG-DNN) classification techniques are used to determine the causes of a given plant disease based on the characteristics of soil images and plant disease images. The findings are communicated to the growers through text messages sent to their mobile phones on a daily and seasonal basis, along with any potential recommendations for preventative actions.

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation.

Plants are the primary source of food for world's population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant's survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

Growth of Rice Plant Under Green Light- Inefficient or Neglected

The objectives of this study was to investigate the effect of green light-emitting diodes (LEDs) on rice (Oryza sativa L.) seed germination as well as seedling growth. The rice seed of BRRIdhan52, BRRIdhan81, BRRIdhan86 and BRRIdhan92 were germinated and nursed in control environment in full dark (no supplemental light) and under green light (6 h/day with18 h dark). The results showed that germination percent, leaf length, leaf blade length, seedling fresh and dry weight were enhanced by green lighting. Highest germination percent (88%) was counted in BRRIdhan86 and longest first leaf (7.24 cm) with widest leaf (7.24 cm) in BRRIdhan92 under green light. Green light not only enhance the first leaf blade length also increased the dry matter production. High positive significant correlation were observed in seedling fresh weight and dry weight. Correlation value (r) was positive and significantly ranged from 0.999 to 0.981. Green light remarkably promote the leaf surface area, root morphology which ultimately contributed to vigorous seedling growth with an increase 6 to 156 times than rice seedlings grown in dark for the studied character. As a result, the effects of green light emitting diodes (LEDs) on rice seed and seedlings for plant cultivation under controlled-environment conditions are promising, and they may affect subsequent phases of growth and development. More research on green light and other solid state light sources are needed for use in plant lighting designs, as well as the ability to match wavelengths to plant photo sensors for better production and influence on plant morphology and metabolism.&#x0D; Environ. Sci. &amp; Natural Resources, 13(1&amp;2): 50-55, 2020

Analysis of Plant Species in Elementary School Textbooks in South Korea

Background and objective This study was conducted to find out the status of plant utilization in the current textbooks by analyzing the plants by grade and subject in the national textbooks for all elementary school grades in the 2015 revised curriculum in Korea. Methods The data collected was analyzed using Microsoft Office Excel to obtain the frequency and ratio of collected plant data and SPSS for Windows 26.0 to determine learning content areas by grade and the R program was used to visualize the learning content areas. Results A total of 232 species of plants were presented 1,047 times in the national textbooks. Based on an analysis of the plants presented by grade, the species that continued to increase in the lower grades tended to decrease in the fifth and sixth grades, the upper grades of elementary school. As for the number and frequency of plant species by subject, Korean Language had the highest number and frequency of plant species. The types of presentation of plants in textbooks were mainly text, followed by illustrations and photos of plants, which were largely used in first grade textbooks. In addition, as for the area of learning contents in which plants are used, in the lower grades, plants were used in the linguistic domain, and in the upper grades, in the botanical and environmental domains of the natural sciences. Herbaceous plants were presented more than woody plants, and according to an analysis of the plants based on the classification of crops, horticultural crops were presented the most, followed by food crops. Out of horticultural crops, flowering plants were found the most diversity with 63 species, but the plants that appeared most frequently were fruit trees that are commonly encountered in real life. Conclusion As a result of this study, various plant species were included in elementary school textbooks, but most of them were horticultural crops encountered in real life depending on their use. Nevertheless, plant species with high frequency have continued a similar trend of frequency from the previous curriculums. Therefore, in the next curriculum, plant learning materials should be reflected according to social changes and students’ preference for plants.

Мобильный телефон (smartphone) как инструмент агрария в системе цифрового земледелия

Remote sensing in agriculture is one of the rapidly developing areas of observation of agricultural biological objects. Combining the latest sensor technologies with smartphones allows us to develop powerful platforms for solving various tasks in agriculture. (Research purpose) The research purpose is in analyzing and evaluating the usage of mobile phones and smartphones to monitor the state of the field and agricultural crops in order to obtain information necessary for making optimal management decisions. (Materials and methods) The article presents the results of scientific research and reviews on the use of mobile phones and smartphones to obtain information about agricultural crops in the precision farming system. (Results and discussion) The software for smartphones in agriculture is considered as a subdivision of precise agricultural technologies for monitoring and protecting crops. Smartphones with the required software enable farmers to receive information from various sensors. Authors reviewed the existing simplest mobile applications for smartphones that allow detecting and identifying biotic stresses by comparing photos of plants taken with images from the library, and applications whose work is based on photographing biological objects and sending them to an expert. It has been revealed that mobile applications created on the basis of convolutional neural networks of deep learning, capable of making independent decisions, are gaining popularity. (Conclusions) The use of smartphones equipped with various sensors and additional software opens up new opportunities for farmers, providing data collection, storage, transmission and processing for making management decisions. The article presents the disadvantages of mobile applications. Further expansion of the scope of smartphones for crop monitoring is possible when developing new software using convolutional neural networks of deep learning.

AI-based Identification of Plant Photographs from Herbarium Specimens

AI-based Identification of Plant Photographs from Herbarium Specimens

Finding Hidden Outliers to Promote the Consistency of Key Morphological Traits and Phylogeny in Dennstaedtiaceae

With the development of open science and technological innovation, using sharing data and molecular biology techniques in the study of taxonomy and systematics have become a crucial component of plants, which undoubtedly helps us discover more hidden outliers or deal with difficult taxa. In this paper, we take Dennstaedtia smithii as an example, based on sharing molecular database, virtual herbarium and plant photo bank, to clarify the outliers that have been hidden in Dennstaedtia and find the key morphological traits with consistent of molecular systematics. In molecular phylogenetic analyses, we used rbcL, rps4, psbA-trnH and trnL-F sequences from 5 new and 49 shared data; the results showed that Dennstaedtia smithii is nested within Microlepia rather than Dennstaedtia. We further studied the morphological characters based on the phylogeny result and found that D. smithii is distinguished from other species of Dennstaedtia by spore ornamentation and the unconnected of grooves between rachis and pinna rachis. According to morphological and molecular phylogenetic studies, our results supported that D. smithii should be a new member of Microlepia and renamed Microlepia smithii (Hook.) Y.H. Yan. Finding hidden outliers can promote the consistency of morphological and molecular phylogenetic results, and make the systematic classification more natural.

Medical, Aromatic, and Narcotic Plants Classification using an Artificial Neural Network

Medical, Aromatic, and Narcotic plants are a natural treasure that grows in the desert without human being interference. They can be used in pharmaceutical industries (medicines), medical usage (medical anesthetic), perfumes industries, and cooking. Thus, they are very useful, available, and can be utilized for the sake of human beings. On the other hand, some of these plants are harmful to our bodies and must be strictly prohibited. So, it is necessary to design and implement an image processing system to detect these plants. This system can be applied by the Ministry of Agriculture and Armed Force. After surveying deserts and taking photos of plants by a small camera attached to a drone, they can be inserted into the system to detect the type of captured plant and take action. In this paper, an automatic computer vision system is proposed to identify six types of desert plants. First, a nine-class collected database is prepared. Second, an artificial neural network-based framework, which uses color, DWT, the ratio between the major and the minor axes of the plants, and Tamura statistical texture features, is employed to classify plants. Outcomes and the results of the suggested system have competed with several techniques such as the SVM, the Naive Bayes, the KNN, the decision tree, and discriminant analysis classifiers. Results reveal that the proposed system has the highest overall recognition rate, which is 94.3%, among other techniques.