Molecular identification and genetic relationships among Alcea (Malvaceae) species by ISSR Markers: A high value medicinal plant

Alcea L. is one of the largest genera of Malvaceae family with nearly 70 species worldwide mainly distributed in SW Asia. According to the latest revision of the family, it is represented by 34 species in the Flora of Iran, among them, 15 species are endemic. It is tough to accurate germplasm/ plant recognition by using morphological characteristics because of its propagation, growing and using. We conducted a molecular data analysis on these plant species due to their importance. We examined 156 plants from 14 species in 16 regions that were selected randomly for this investigation. It has been 119 polymorphic bands (94.33%) were resulted from 128 bands of 10 primers in amplification of genomic DNA. ISSR primers have a great capacity to detect polymorphic loci among Alcea species, as evidenced by the high average PIC and MI values found. The genetic similarity of 14 species was calculated and ranged between 0.635 to 0.990. Inter-Simple sequence repeats (ISSR) markers research revealed that Alcea tarica Pakravan & Ghahreman and Alcea kopetdaghensis lljin had the least similarity, while Alcea semnanica Pakravan and Alcea mazandaranica Pakravan & Ghahreman had the most. The current study attempts to answer three questions: 1) can ISSR markers identify Alcea species? 2) what is the genetic structure of these taxa in Iran? and 3) what is the inter-relationship between these taxa? The current study discovered that ISSR markers can be used to identify species.

GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment

Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.

Coexpression of Three Odorant-Binding Protein Genes in the Foreleg Gustatory Sensilla of Swallowtail Butterfly Visualized by Multicolor FISH Analysis

Lepidopteran insects are mostly monophagous or oligophagous. Female butterflies distinguish their host plants by detecting a combination of specific phytochemicals through the gustatory sensilla densely distributed on their foreleg tarsi, thereby ensuring oviposition on appropriate host plants. In this study, to gain insight into the molecular mechanism underlying host plant recognition by the gustatory sensilla, using Asian swallowtail, Papilio xuthus, we focused on a family of small soluble ligand-binding molecules, odorant-binding proteins (OBPs), and found that three OBP genes showed enriched expression in the foreleg tarsus. Multicolor fluorescence in situ hybridization analyses demonstrated the coexpression of these three OBP genes at the bases of the foreleg gustatory sensilla. Further analyses on other appendages revealed that PxutOBP3 was exclusively expressed in the tissues which could have direct contact with the leaf surface, suggesting that this OBP gene specifically plays an important role in phytochemicals perception.

Implementasi Pendekatan Saintifik Dalam Meningkatkan Pengenalan Tanaman Pada Anak-Anak Kelompok A Di TK Yudistira Kumara II Sembung Kecamatan Mengwi Kabupaten Badung

Early childhood is a sensitive period or golden age, where there is rapid development so that teachers must be smart in choosing a learning approach so that learning objectives can be achieved properly, one of which is a scientific approach. The implementation of the scientific approach is intended to provide understanding to students in recognizing, understanding various materials using a scientific approach, that information can come from anywhere, not depending on unidirectional information from the teacher. Scientific learning teaches children to find new knowledge, solve problems, think critically and create creativity so that it helps them understand the environment, especially plants, collect and process information as the basic keys for children to think broadly. Therefore the expected learning conditions are directed to encourage students to find out from various sources through observation, and not just being told. The theory used to analyze the problem is Piaget's theory of cognitivism. This research is a classroom action research with the method of documentation, observation and interviews from the implementation of the cycle, the observation method is carried out using an observation format which includes three indicators including, indicator I: mentioning the kinds of plants, indicator II: counting and classifying the kinds of plants according to type III indicator: distinguishing parts, types and forms of plants and mentioning the color of the plant. The results of the observations obtained from the learning outcomes in each cycle were analyzed quantitatively with descriptive statistics using the percentage technique. The subjects of this research were the children of group A TK Yudistira Kumara II Sembung. The results showed that with a scientific approach it could improve the ability to recognize plants in group A in TK Yudistira Kumara II Sembung. The percentage of the initial state, cycle I and cycle II shows an increase. The results of research on the implementation of the scientific approach in improving plant recognition were resolved in Cycle II and the research was successful

A Hybrid Approach for Automated Plant Leaf Recognition Using Hybrid Texture Features and Machine Learning-Based Classifiers

Automated plant recognition performs a significant role in various applications used by environmental experts, chemists, and botany experts. Humans can recognize plants manually, but it is a prolonged and low-efficiency process. This paper introduces an automated system for recognizing plant species based on leaf images. A hybrid texture and colour-based feature extraction method was applied on digital leaf images to produce robust feature, and a further classification model was developed. A combination of machine learning methods, such as SVM (support vector machine), KNN (k-nearest neighbours), and ANN (artificial neural network), was applied on dataset for plant classification. This dataset contains 32 types of leaves. The outcomes of this work proved that success rate of plant recognition can be enhanced up to 94% with ANN classifier when both shape and colour features are utilized. Automatic recognition of plants is useful for medicine, foodstuff, and reduction of chemical wastage during crop spraying. It is also useful for identification and preservation of species.

Leaf recognition using BP-RBF hybrid neural network

Plant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and background. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recognition. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%, highest among all the classifiers.