MedLeaf: Mobile Application for Medicinal Plant Identification Based on Leaf Image

This research proposes MedLeaf as a new mobile application for medicinal plants identification based on leaf image. The application runs on the Android operating system. MedLeaf has two main functionalities, i.e. medicinal plants identification and document searching of medicinal plant. We used Local Binary Pattern to extract leaf texture and Probabilistic Neural Network to classify the image. In this research, we used30 species of Indonesian medicinal plants and each species consists of 48 digital leaf images. To evaluate user satisfaction of the application we used questionnaire based on heuristic evaluation. The evaluation result shows that MedLeaf is promising for medicinal plants identification. MedLeaf will help botanical garden or natural reserve park management to identify medicinal plant, discover new plant species, plant taxonomy and so on. Also, it will help individual, groups and communities to find unused and undeveloped their skill to optimize the potential of medicinal plants. As the results, MedLeaf will increase of their resources, capitals, and economic wealth

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%

Method and approach Mapping for Agri-food Supply Chain Risk Management: A literature review

One of the agri-food characteristics is perishable product which made it has a higher chance damage risk from the farmer to the consumer. While issues around food security and associated risks are extremely important. Some methods or approaches have been used to identify and assess risks that occur in agri-food supply chain. The purpose of this paper was to identify the development of methods or approaches used to identify and assess the risks that occured in the agri-food supply chain. The articles search was undertaken through articles search on selected relevant journals of supply chain risk management for agri-food published from 2004 until 2014. A total of 77 randomly selected journal articles had been analyzed. These mapping were arranged in systematic stages, started from searches related supply chain risk management for agrifood. Furthermore, the articles identified and classified the methods or approaches for each stage of supply chain risk management, and were divided into three main stages: risk identification, risk assessment and risk mitigation. The last, the articles of risk identification are categorized into three groups : qualitative, semi-quantitative and qualitative.The mapping results showed that risk assessment supply chain for agri-food was much related to microbiology risk assessment. It related to the characteristics of agri-food products. Standard models used for risk assessment in supply chain for agri-food were based on integration of statistical analysis and simulation. The other techniques used included intelligent technique, optimization models and multi-criteria decision analysis. The literature on supply chain risk management for agri-food is so vast, complex and difficult to understand that a mapping of method and approach is needed and much value for the research community. Keywords :supply chain risk, risk identification, risk assessment, risk mitigation, agri-foo

Similarities of Antimalarial Resistance Genes in Plasmodium Falciparum Based on Ontology

The finding of P. falciparum chloroquine resistance (pfcrt) and P. falciparum multidrug resistance 1(pfmdr1) gene in P. falciparum has become an obstacle in treating malaria. The polymorphism between the two genes may result in molecular functions, in cellular components, or in biological processes. The objective of this research is to find similarities between the two genes in 3 components; cellular components, molecular functions and biological processes, based on Gene Ontology. the similarity will be counted semantically by path length approach with Wang method. The range of similarity values is 0-1. After the similarity value examined; in Molecular Function the similarity is 1 due to the same drug transmembrane transporter activity, in Cellular Component is 0,714, the similarity only at the same vacuole food cells, and in Biological Processes is 1 due to the same proces in responding to drug. Therefore, this research proves both genes have similarities based on gene ontology

Morphological Feature Extraction of Jabon’s Leaf Seedling Pathogen using Microscopic Image

This research aims to analyze morphological techniques for feature extraction of Jabon’s leaf seedling pathogen using digital microscopic image. The kinds of the pathogen were Curvularia sp., Colletotrichum sp., and Fusarium sp.. Pathogens or causes of disease were identified manually based on macroscopic and microscopic observation of morphological characters. Morphological characters describe the characteristics of shape, color and size of a pathogen structure. We focused on shape feature by using the morphological techniques to feature extraction. The morphology features extraction used were area, perimeter, convex area, convex perimeter, compactness, solidity, convexity, and roundness. The methodologies were acquisition, preprocessing, features extraction and data analysis for derivative features. With features extraction, we got the pattern that described each pathogen for pathogen identification. From the experimental result showed that compactness and roundness feature were able to differentiate each pathogen due to that the characteristics of each pathogen class were separated

Leaf Morphological Feature Extraction of Digital Image Anthocephalus Cadamba

This research implemented an image feature extraction method using morphological techniques. The goal of this proccess is detecting objects that exist in the image. The image is converted into a grayscale image format. Then, grayscale image is processed with tresholding method to get initial segmentation. Furthermore, image from segmentation results are calculated using morphological methods to find the mapping of the original features into the new features. This process is done to get better class separation. Research conducted on two Antocephalus cadamba (Jabon) leaf diseased seedlings data set image that contained leaf spot disease and leaf blight. The results obtained morphological features such as rectangularity, roundness, compactness, solidity, convexity, elongation, and eccentricity able to represent the characteristic shape of the symptoms of the disease. All properties form the symptoms can be quantitatively explained by the features form. So it can be used to represent type of symptoms of two diseases in Antocephalus cadamba (Jabon)

Weighted Ensemble Classifier for Plant Leaf Identification

Plant leaf identification using image can be constructed by ensemble classifier. Ensemble classifier executes classification of various features independently. This experiment utilized texture feature and geometry feature of plant leaf to find out which features are more powerful. Each classifier trained by specific feature produced different accuracy rate. To integrate ensemble classifier the results of the classification were weighted, so as the score obtained from better features contributed greater to the final results. Weighted classification results were combined to get the final result. The proposed method was evaluated using dataset comprises of 156 variety of plants with 4559 images. Weighting and combining classifier used in this study were Weighted Majority Vote (WMV) and Naïve Bayes Combination. Both of those method result showed better accuracy than using single classifier. The average accuracy of single classifier was 61.2% for geometry classifier and 70.3% for texture classifier, while WMV method was 77.8% and Naïve Bayes Combination was 94.6%. The calculation of classifier’s weight by using WMV method produces a weight value of 0.54 for texture feature classifier and 0.46 for geometry feature classifier

Identifikasi Cendawan Patogen Penyebab Penyakit pada Daun Jabon Merah (Anthocephalus macrophyllus (Roxb.) Havil)

Red Jabon (Anthocephalus macrophyllus (Roxb.) Havil) is a forest plant that can replace sengon (Falcataria moluccana) because it has advantages. The demand for red jabon wood is increasing, but the cultivation of red jabon is still constrained by the attack of pathogens that cause leaf disease. The attack of these pathogens can cause damage and death of seedlings so that it can be economically detrimental. The aim of this study was to identify the pathogens that cause disease in red Jabon. The method used is the Postulate Koch method, which starts from sampling red leaves with leaf disease symptoms, isolation, inoculation and analysis of disease incidence, re-isolation, and identification. Based on the identification results found as many as 2 pathogenic fungi that cause leaf spot disease, namely Pestalotia sp. and Rhizoctonia sp. In addition, 6 pathogenic fungi were found that cause blight, namely Lasiodiplodia theobromae, Fusarium sp., Colletrotrichum sp., Marssonina sp., Gloeosporium sp. 1, Gloeosporium sp. 2. Keywords: identification, percentage of disease incidence, Postulat Koch, red jabo