Utilizing machine-learning-based 3D image analysis for classifying charred grape seeds to the varietal level

Abstract Grapevine (Vitis vinifera L.) is an essential part of the oldest group of fruit trees around which horticulture evolved, currently includes thousands of cultivars, grown at numerous climatic conditions. Discrimination between these varieties has been traditionally conducted using ampelography, and 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, aiming to utilize these methods for the identification of fresh and archaeological specimens. Here, we present for the first time a highly accurate varietal classification tool, using an innovative and accessible approach for 3D seed scanning. The suggested classification methodology is machine-learning-based, using a complete set of 3D data obtained for each seed, applied with the Iterative Closest Point (ICP) registration algorithm and the Linear Discriminant Analysis (LDA) technique. This methodology achieved classification results of ca. 90-99% accuracy when trained by fresh seeds to test unknown fresh seeds. Moreover, the classification of charred seeds reached up to 100% accuracy when trained by charred seeds. Based on this approach, our long-term aim is to develop a computerized classification tool for the identification of grape and possibly other species and varieties. Such a tool can significantly improve the fields of archaeobotany, as well as general taxonomy.

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

Scientific Reports ◽

10.1038/s41598-021-92559-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Vlad Landa ◽

Yekaterina Shapira ◽

Michal David ◽

Avshalom Karasik ◽

Ehud Weiss ◽

...

Keyword(s):

Machine Learning ◽

Morphological Diversity ◽

Grape Seeds ◽

Linear Discriminant ◽

Varietal Classification ◽

Genomic Approach ◽

Archaeobotanical Remains ◽

Classification Tool ◽

Suggested Classification

AbstractGrapevine (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.

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Use of Leaf and Fruit Morphometric Analysis to Identify and Classify White Mulberry (Morus alba L.) Genotypes

Agriculture ◽

10.3390/agriculture8100157 ◽

2018 ◽

Vol 8 (10) ◽

pp. 157 ◽

Cited By ~ 1

Author(s):

Riccardo Lo Bianco ◽

Fabio Mirabella

Keyword(s):

Image Analysis ◽

Leaf Size ◽

Morus Alba ◽

Leaf Margin ◽

Morphometric Traits ◽

Linear Discriminant ◽

White Mulberry ◽

Leaf Size And Shape ◽

Classification Tool ◽

Apical Tooth

Digital image analysis and multivariate data analysis were used in this study to identify a set of leaf and fruit morphometric traits to discriminate white mulberry (Morus alba L.) cultivars. The trial was conducted using three- to five-year-old potted cuttings of several white mulberry cultivars. 32 leaf morphometric descriptors were recorded in 2011 and 2012 from 11 mulberry cultivars using image analysis of scanned leaves, whereas six fruit descriptors were recorded in 2011 from nine mulberry cultivars. Linear discriminant analysis (LDA) was used to identify a subset of measured variables that could discriminate the cultivars in trial. Biplot analysis, followed by cluster analysis, was performed on the discriminant variables to investigate any possible cultivar grouping based on similar morphometric traits. LDA was able to discriminate the 11 cultivars with a canonical function, which included 13 leaf descriptors. Using those 13 descriptors, the Biplot showed that over 84% of the variability could be explained by the first three factors. Clustering of standardized biplot coordinates recognized three groups: the first including ‘Korinne’ and ‘Miura’ with similar leaf angles and apical tooth size; the second including ‘Cattaneo’, ‘Florio’, ‘Kokusò-21’, ‘Kokusò-27’, and ‘Kokusò Rosso’ with similar leaf size and shape; and the third including ‘Ichinose’, ‘Kayrio’, ‘Morettiana’, and ‘Restelli’, with similar leaf margin. Fruit descriptors were fewer and measured on fewer cultivars, yielding smaller discriminatory power than leaf descriptors. Use of leaf morphometric descriptors, along with image and multivariate analysis, proved to be effective for discriminating mulberry cultivars and showed promise for the implementation of a simple and inexpensive characterization and classification tool.

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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues

Breast Cancer Research and Treatment ◽

10.1007/s10549-021-06243-2 ◽

2021 ◽

Author(s):

Shinya Sato ◽

Satoshi Maki ◽

Takashi Yamanaka ◽

Daisuke Hoshino ◽

Yukihide Ota ◽

...

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Breast Biopsy

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Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106158 ◽

2021 ◽

Vol 185 ◽

pp. 106158

Author(s):

Maryam Bayatvarkeshi ◽

Suraj Kumar Bhagat ◽

Kourosh Mohammadi ◽

Ozgur Kisi ◽

M. Farahani ◽

...

Keyword(s):

Machine Learning ◽

Soil Temperature ◽

Air Temperature ◽

Climatic Conditions ◽

Learning Models ◽

Machine Learning Models

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Image analysis and machine learning based malaria assessment system

Digital Communications and Networks ◽

10.1016/j.dcan.2021.07.011 ◽

2021 ◽

Author(s):

Kyle Manning ◽

Xiaojun Zhai ◽

Wangyang Yu

Keyword(s):

Machine Learning ◽

Image Analysis ◽

Assessment System

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Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽

10.3390/ani11010050 ◽

2020 ◽

Vol 11 (1) ◽

pp. 50

Author(s):

Jennifer Salau ◽

Jan Henning Haas ◽

Wolfgang Junge ◽

Georg Thaller

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Body Parts ◽

Nearest Neighbour ◽

Data Set ◽

3D Data ◽

Holstein Friesian ◽

Knn Classification ◽

Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

Remote Sensing ◽

10.3390/rs11101181 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1181 ◽

Cited By ~ 1

Author(s):

Norman Kerle ◽

Markus Gerke ◽

Sébastien Lefèvre

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Image Analysis ◽

The Netherlands ◽

Object Based Image Analysis ◽

Biennial Conference ◽

Object Based ◽

The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

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