Biometric Analysis of Leaf Venation Density Based on Digital Image

The density level in the leaf venation type has different characteristics. These different characteristics explain the environment in which plants grow, such as habitat, vegetation, physiology and climate. This research aims to measure of leaf venation density, leaf venation feature analysis and then identifying plants based on venation type. Stages of this research include leaf image data collection, segmentation, vein detection, feature extraction, feature selection, classification, evaluation and ending with analysis. The results of this study indicate that the level of leaf venation density is quite good is the type of venation paralellodromous, acrodromous and pinnate. Based on the selection of features using Boruta Algorithm, obtained 19 most important features that represent the type of leaf venation. This is reinforced by the average of accuracy produced at the time of classification using SVM, which amounted to 77.57%

Efektivitas Metode Field Trip Dengan Aplikasi PlantNet Pada Materi Spermatophyta Sebagai Alternatif Inovasi Pembelajaran: (The Effectiveness of Field Trip Method Using PlantNet Applications on Spermatophyte Concepts as an Alternative Learning Innovatio)

Learning system during a pandemic requires everything to be done online. This is a challenge for teachers to continue applying certain learning methods without eliminating the essence and purpose of the method. The innovation of the field trip method with technology application is expected to be an alternative of learning. Thus, this study aims to analyze the effectiveness of using the PlantNet Application on identifying and classifying of Spermatophyte Plants. This study used a qualitative descriptive method and the subjects of this research are high school students of grade X. The analyzed aspects in this research are learning outcomes, PlantNet use and user responses. The results of this study indicate that 88% of students achieve good result and this can be stated as a successful learning innovation based on individual completeness. Regarding the use of PlantNet, almost all of the indicators received very good ratings by students. For user responses, teachers and students have a good to very good response to the use of the PlantNet application. Abstrak. Pembelajaran di masa pandemi mengharuskan segala sesuatu dilakukan secara daring. Hal ini menjadi tantangan bagi guru untuk tetap menerapkan metode pembelajaran tertentu tanpa harus menghilangkan esensi dan tujuan dari metode tersebut. Inovasi metode field trip dengan bantuan aplikasi diharapkan menjadi sebuah alternatif pembelajaran. Sehingga, penelitian ini bertujuan untuk menganalisis efektivitas penggunaan aplikasi PlantNet pada materi identifikasi dan klasifikasi Spermatophyta. Penelitian ini menggunakan metode deskriptif kualitatif dengan subjek penelitian Siswa SMA Kelas X. Aspek-aspek yang dianalisis yaitu hasil belajar, fungsi PlantNet dan respon pengguna. Hasil penelitian ini menunjukkan 88% siswa mencapai ketuntasan belajar atau lebih dari ketuntasan individu 85%, sehingga pembelajaran dikatakan berhasil. Terkait fungsi PlantNet, hampir semua indikator fungsi memperoleh penilaian yang sangat baik oleh siswa. Untuk respon pengguna, guru dan siswa  memiliki respon yang baik hingga sangat baik terhadap penggunaan aplikasi PlantNet

PENERAPAN PENDEKATAN MACHINE LEARNING PADA PENGEMBANGAN BASIS DATA HERBAL SEBAGAI SUMBER INFORMASI KANDIDAT OBAT KANKER

Cancer is still an epidemiological disease in Indonesia. Drug development against cancer still relies to pharmacological laboratories and natural chemicals, which could have side effects. Cancer drug development has entered the stage of molecular biology, where the interaction of ligand chemical structure with receptor protein can be studied with high accuracy. Various chemical compounds, ranging from synthetic, semi-synthetic, to natural materials, developed for the purpose to fight one of the most dangerous diseases. In the context of the development of herbal-based drugs, there has been found heaps of natural compounds, curated and annotated, in various databases belonging to China, Taiwan, Indonesia, Japan, and several other countries. However, problems arise when choosing the best bioactive compounds to develop against cancer. Complexity arises because the metabolic pathway of cancer is very diverse, depending on the type and phase of cancer. Therefore, in this systematic review, we developed a machine learning approach to screen for these bioactive compounds, then took the best candidates for molecular simulation operations that would be tested for validity in wet experiments. Thus, the automation of the candidate drug development process for cancer could be achieved with great significance. It is known that the most effective and efficient machine learning method was Naïve Bayes, but the best in processing large amounts of compound data was classfier SVM. The future of complex bioactive compounds data could be secured by employing deep learning method. Keywords: machine learning, drug development, natural material compounds, metabolic pathways, cancer

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Identification of Venation Type Based on Venation Density using Digital Image Processing

Leaf venation is one biometric feature of leaves that have an important role in growth processes of the plant, and to determine the relationship of the plant physiology and the environment in which plants grow. At every different environment, plants have different types of leaf venation. It can be seen from the level of the leaf vein density. In this study, the feature of leaf vein density was used to identify the leaves based on venation type. The venation density features obtained from segmentation, vein detection, and density feature extraction of leaf venation. Identification of the venation type was made using the artificial neural network (ANN). The results of this study indicate that the proposed method can classify the leaf correctly image based on the venation type. On the dataset with 324 samples, the accuracy of 82.71% was obtained. This shows that the leaf vein density features allow use as a plant identifier.Keywords: leaf vein density, vein detection, density feature extraction, artificial neural networ

PlantKViT: A Combination Model of Vision Transformer and KNN for Forest Plants Classification

The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification

Chemical profile, total phenolic content, DPPH free radical scavenging and α-glucosidase inhibitory activities of Cosmos caudatus Kunth leaves

Herbs and medicinal plants are major sources of traditional or folk medicines for many countries of the world, including Malaysia. This study evaluated the bioactive potential of the leaf ethanolic extract and solvent fractions of Cosmos caudatus Kunth, in scavenging free radicals and inhibiting the enzyme α-glucosidase. In addition, their metabolite profiles were also characterized using liquid chromatography–mass spectrometry. The bioactivity was found to be concentrated in the EtOAc and BuOH fractions which largely contained rutin, quercetin 3-O-galactoside, quercetin 3-O-glucoside, quercetin 3-O-xyloside, quercetin 3-O-arabinofuranoside, quercetin 3-O-rhamnoside, and quercetin 3-O-galactoside, as profiled by LC-MS/MS. It was further shown that the flavonoids glycosides contributed to the free radical scavenging and glucose lowering effects of C. caudatus leaves. The results indicated that the leaves of C. caudatus are a rich source of bioactive compounds and could be prospective materials for development of new anti-diabetic agents

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy