Accurate classification of fresh and charred grape seeds to the varietal level, using machine learning based classification method

Grapevine (Vitis vinifera L.) currently includes thousands of cultivars. Discrimination between these varieties, historically done by ampelography, is done in recent decades mostly by genetic analysis. However, when aiming to identify archaeobotanical remains, which are mostly charred with extremely low genomic preservation, the application of the genomic approach is rarely successful. As a result, variety-level identification of most grape remains is currently prevented. Because grape pips are highly polymorphic, several attempts were made to utilize their morphological diversity as a classification tool, mostly using 2D image analysis technics. Here, we present a highly accurate varietal classification tool using an innovative and accessible 3D seed scanning approach. The suggested classification methodology is machine-learning-based, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of 91% to 93% accuracy in average when trained by fresh or charred seeds to test fresh or charred seeds, respectively. We show that when classifying 8 groups, enhanced accuracy levels can be achieved using a "tournament" approach. Future development of this new methodology can lead to an effective seed classification tool, significantly improving the fields of archaeobotany, as well as general taxonomy.

Pronounced Genetic Separation Among Varieties of thePrimula cusickianaSpecies Complex, a Great Basin Endemic

Distinguishing between unique species and populations with strong genetic structure is a common challenge in population genetics, especially in fragmented habitats where allopatric speciation may be widespread and distinct groups may be morphologically similar. Such is often the case with species complexes across sky island environments. In these scenarios, biogeography may help to explain the relations between species complex members, and RADseq methods are commonly used to compare closely related species across thousands of genetic loci. Here we use RADseq to clarify the relations between geographically distinct but morphologically similar varieties of thePrimula cusickianaspecies complex, and to contextualize past findings of strong genetic structure among populations within varieties. Our genomic analyses demonstrate pronounced separation between isolated populations of this Great Basin endemic, indicating that the current varietal classification of complex members is inaccurate and emphasizing their conservation importance. We discuss how these results correspond to recent biogeographical models used to describe the distribution of other sky island taxa in western North America. Our findings also fit into a wider trend observed for alpinePrimulaspecies complexes, and we consider how heterostylous breeding systems may be contributing to frequent diversification via allopatric speciation in this genus.

Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties

Grafting by vegetables is a practice with many benefits, but also with some unknown influences on the chemical composition of the fruits. Our goal was to assess the effects of grafting and storage on the extracted juice of four orange-fleshed Cantaloupe type (Celestial, Donatello, Centro, Jannet) melons and two green-fleshed Galia types (Aikido, London), using sensory profile analysis and analytical instruments: An electronic tongue (E-tongue) and near-infrared spectroscopy (NIRS). Both instruments are known for rapid qualitative and quantitative food analysis. Linear discriminant analysis (LDA) was used to classify melons according to their varieties and storage conditions. Partial least square regression (PLSR) was used to predict sensory and standard analytical parameters. Celestial variety had the highest intensity for sensory attributes in Cantaloupe variety. Both green and orange-fleshed melons were discriminated and predicted in LDA with high accuracies (100%) using the E-tongue and NIRS. Galia and Cantaloupe inter-varietal classification with the E-tongue was 89.9% and 82.33%, respectively. NIRS inter-varietal classification was 100% with Celestial variety being the most discriminated as with the sensory results. Both instruments, classified different storage conditions of melons (grafted and self-rooted) with high accuracies. PLSR showed high accuracy for some standard analytical parameters, where significant differences were found comparing different varieties in ANOVA.

Research on Germination Prediction and Varietal Classification for Rice Seed Quality

In agriculture most of the task done manually by experienced persons. They made decision on the basis of what they feel and see. The prediction result also not giving expected results. For getting the best yield the selection of quality seed is mandatory. But the manual analysis cannot assure the best quality seed. Rice Seed quality estimation can be done by considering the textural features of rice seed image. For this we are going to propose Digital Image processing Techniques to classify and grade the quality of the seed. There are number of digital image processing techniques proposed for classifying the variety of seed and predicting the germination rate of seed. In this paper we are going to summarize the hardware setup, varieties, features extracted, methods or algorithms used and result they obtained. In future we are going to propose a simple grading system for the rice seed quality system can be used by formers.