scholarly journals The Weed Plant Detection

2021 ◽  
Vol 10 (4) ◽  
pp. 206-209
Author(s):  
Geetha V. ◽  
Gomathy CK ◽  
Y.Padmini Reddy ◽  
Haripriya V.
Keyword(s):  
Optical Sensors ◽  
Clustering Algorithms ◽  
Specific Treatment ◽  
Feature Space ◽  
Gps Data ◽  
Site Specific ◽  
Weed Plants ◽  
Processing Algorithms ◽  
Form Features ◽  
Complex Cases

The Knowledge about the distribution of weeds within the sector could also be prerequisite for the site-specific treatment. Optical sensors changes to detect vary weed densities and species which can have mapped using GPS data. Weeds are extracted from the pictures that are using the image processing and therefore the report by the form features. The classification supported the features reveal the type and therefore the number of weeds per the image. For the classification the sole maximum of sixteen features out of the eighty-one computed ones is employed. Which enables the optimal distinction of weed classes is used the choice is usually done using processing algorithms, which the speed discriminate of the features of prototypes. If no prototypes are available, clustering algorithms are often used to automatically generate clusters. Within the next step weed classes are often assigned to the clusters. Such procedure aids to select prototypes, which are completed manually. Classes are often identified, that are distinct within the feature space or which are overlapping, and thus not well separable. The clustering is usually utilized in some, less complex cases to work out automatic procedure for the classification. By using the system weed plants are generated. These are differentiating to the results of manual weeds sampling.

Site-specific wild oat (Avena fatua L.) management

2012 ◽  
Vol 92 (5) ◽  
pp. 923-931 ◽  
Author(s):  
H. J. Beckie ◽  
S. Shirriff
Keyword(s):  
Specific Treatment ◽  
Triticum Aestivum L ◽  
Avena Fatua ◽  
Slope Position ◽  
Wild Oat ◽  
Herbicide Application ◽  
Brassica Napus L ◽  
Site Specific ◽  
Lower Slope ◽  
Upper Slope

Beckie, H. J. and Shirriff, S. 2012. Site-specific wild oat ( Avena fatua L.) management. Can. J. Plant Sci. 92: 923–931. Variation in soil properties, such as soil moisture, across a hummocky landscape may influence wild oat emergence and growth. To evaluate wild oat emergence, growth, and management according to landscape position, a study was conducted from 2006 to 2010 in a hummocky field in the semiarid Moist Mixed Grassland ecoregion of Saskatchewan. The hypothesis tested was that wild oat emergence and growth would be greater in lower than upper slope positions under normal or dry early growing season conditions. Three herbicide treatments were imposed on the same plots each year of a 2-yr canola (Brassica napus L.) – wheat (Triticum aestivum L.) sequence: (1) nontreated (weedy) control; (2) herbicide application to upper and lower slope positions (i.e., full or blanket application); and (3) herbicide application to lower slope position only. Slope position affected crop and weed densities before in-crop herbicide application in years with dry spring growing conditions. Site-specific wild oat herbicide application in hummocky fields in semiarid regions may be justified based on results of wild oat control averaged across slope position. In year 2 of the crop sequence (wheat), overall (i.e., lower and upper slope) wild oat control based on density, biomass, and dockage (i.e., seed return) was similar between site-specific and full herbicide treatment in 2 of 3 yr. Because economic thresholds have not been widely adopted by growers in managing wild oat, site-specific treatment in years when conditions warrant may be an appropriate compromise between no application and blanket herbicide application.

scholarly journals Multi-View Clustering in Latent Embedding Space

2020 ◽  
Vol 34 (04) ◽  
pp. 3513-3520 ◽  
Author(s):  
Man-Sheng Chen ◽  
Ling Huang ◽  
Chang-Dong Wang ◽  
Dong Huang
Keyword(s):  
Structure Learning ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Global Structure ◽  
Optimization Scheme ◽  
Optimization Framework ◽  
Novel Approach ◽  
Indicator Matrix ◽  
Original Feature

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. Specifically, in our framework, a latent embedding representation is firstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.

The fast clustering algorithm for the big data based on K-means

2020 ◽  
Vol 18 (06) ◽  
pp. 2050053
Author(s):  
Ting Xie ◽  
Taiping Zhang
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Data Sets ◽  
Benchmark Data ◽  
Clustering Model ◽  
Alternating Direction ◽  
Learning Technique ◽  
Noise Data

As a powerful unsupervised learning technique, clustering is the fundamental task of big data analysis. However, many traditional clustering algorithms for big data that is a collection of high dimension, sparse and noise data do not perform well both in terms of computational efficiency and clustering accuracy. To alleviate these problems, this paper presents Feature K-means clustering model on the feature space of big data and introduces its fast algorithm based on Alternating Direction Multiplier Method (ADMM). We show the equivalence of the Feature K-means model in the original space and the feature space and prove the convergence of its iterative algorithm. Computationally, we compare the Feature K-means with Spherical K-means and Kernel K-means on several benchmark data sets, including artificial data and four face databases. Experiments show that the proposed approach is comparable to the state-of-the-art algorithm in big data clustering.

scholarly journals O3.4. PSYCHOSIS PHENOTYPES FROM B-SNIP FOR CLINICAL ADVANCES: BIOTYPE CHARACTERISTICS AND TARGETS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S7-S7
Author(s):  
Brett Clementz ◽  
Rebekah Trotti ◽  
Godfrey D Pearlson ◽  
Matcheri Keshavan ◽  
Elliot Gershon ◽  
...  
Keyword(s):  
Clinical Features ◽  
Numerical Taxonomy ◽  
Negative Symptoms ◽  
Clinical Manifestations ◽  
Specific Treatment ◽  
Feature Space ◽  
Biological Research ◽  
Clinical Feature ◽  
Treatment Targets ◽  
Intermediate Phenotypes

Abstract Background Psychiatry aspires to disease understanding and precision medicine. Biological research supporting such missions in psychosis may be compromised by continued reliance on clinical phenomenology in the search for pathophysiological mechanisms. A transdiagnostic deep phenotyping approach, such as that used by the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP), offers a promising strategy for discovery of biological mechanisms underlying psychosis syndromes. The B-SNIP consortium has identified biological subtypes of psychosis, Biotypes, which outperform conventional DSM diagnoses when accounting for variance of multiple external validating measures. While these biological distinctions are scientifically remarkable, their resulting clinical manifestations and potential utility in clinical practice is of paramount importance. Methods Approximately 1500 psychosis cases and 450 healthy persons were administered the B-SNIP biomarker battery (including MRI, EEG, ocular motor, and cognition measures). Psychosis cases were also clinically characterized using multiple measures, including MADRS, PANSS, YMRS, and Birchwood. Numerical taxonomy approaches were used for identifying biologically homogenous psychosis subgroups (gap and TWO-STEP cluster identifications, k-means clustering, and canonical discriminant analysis). ANOVA models were used to analyze external validating measures. Multivariate discriminant models were used to identify clinical features differentiating conventional psychosis syndromes and psychosis Biotypes. Results There was remarkable similarity between previously published biomarker profiles for DSM psychosis syndromes and a new sample of psychosis cases (average r=.92). Numerical taxonomy on biomarker data recovered three subgroups (replicating previous findings), and the biomarker profiles were highly similar to previous results (average r=.87). Schizoaffective cases were both the most diverse and the most clearly differentiated from schizophrenia and bipolar cases (on conative negative symptoms, depression, and mania) in clinical feature space. The only feature that uniquely distinguished schizophrenia was social-relational negative symptoms. Biotype-1 was characterized by accentuations on clinical features consistent with their biomarker deviations (relational negative symptoms, poor social functioning, and dysfunction of cognition). Alternatively, Biotype-2, also consistent with their biomarker deviations, had clinical features indicating neurophysiological dysregulation (most specifically physiological and behavioral dysregulation). Biotype-3 cases, the most normal across biomarkers, were noticeably absent of Biotype-1 clinical features and had more restricted clinical manifestations than any other Biotype or DSM subgroup. We illustrate three possible Biotype-specific treatment targets. Discussion Replication of B-SNIP psychosis Biotypes indicates the possible utility and importance of neurobiological subtyping within psychosis that can yield specific treatment targets. In an analysis of clinical features, B-SNIP found that Biotypes have unique and defining clinical features that are consistent with their neurobiological profiles. Biotypes and DSM psychosis subgroups are neither neurobiologically nor clinically redundant. Specific treatment targets for psychosis Biotypes are not derivable from conventional clinical psychosis diagnoses. B-SNIP outcomes provide a background for future work that could establish psychiatry as a laboratory discipline, at least with regard to care of psychosis patients. This path is hypothetical at the moment but aspirational for the field.

scholarly journals Site‐specific treatment outcome in smokers following 12 months of supportive periodontal therapy

2016 ◽  
Vol 43 (12) ◽  
pp. 1086-1093 ◽  
Author(s):  
Dagmar F. Bunæs ◽  
Stein Atle Lie ◽  
Anne Nordrehaug Åstrøm ◽  
Kamal Mustafa ◽  
Knut N. Leknes
Keyword(s):  
Treatment Outcome ◽  
Specific Treatment ◽  
Periodontal Therapy ◽  
Site Specific ◽  
Supportive Periodontal Therapy

scholarly journals Emergent community agglomeration from data set geometry

2017 ◽  
Author(s):  
Chenchao Zhao ◽  
Jun S. Song
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Curse Of Dimensionality ◽  
Dimensional Euclidean Space ◽  
Dynamical Process ◽  
Data Sets ◽  
Learning Methods ◽  
Data Set ◽  
Automatic Grouping

In the statistical learning language, samples are snapshots of random vectors drawn from some unknown distribution. Such vectors usually reside in a high-dimensional Euclidean space, and thus, the "curse of dimensionality" often undermines the power of learning methods, including community detection and clustering algorithms, that rely on Euclidean geometry. This paper presents the idea of effective dissimilarity transformation (EDT) on empirical dissimilarity hyperspheres and studies its effects using synthetic and gene expression data sets. Iterating the EDT turns a static data distribution into a dynamical process purely driven by the empirical data set geometry and adaptively ameliorates the curse of dimensionality, partly through changing the topology of a Euclidean feature space into a compact hypersphere. The EDT often improves the performance of hierarchical clustering via the automatic grouping information emerging from global interactions of data points. The EDT is not restricted to hierarchical clustering, and other learning methods based on pairwise dissimilarity should also benefit from the many desirable properties of EDT.

scholarly journals Unsupervised learning of Swiss population spatial distribution

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246529
Author(s):  
Mikhail Kanevski
Keyword(s):  
Spatial Distribution ◽  
Unsupervised Learning ◽  
Population Distribution ◽  
Expert Knowledge ◽  
Clustering Algorithms ◽  
Growth Curves ◽  
Feature Space ◽  
Point Patterns ◽  
Swiss Population ◽  
Spatially Distributed

The paper deals with the analysis of spatial distribution of Swiss population using fractal concepts and unsupervised learning algorithms. The research methodology is based on the development of a high dimensional feature space by calculating local growth curves, widely used in fractal dimension estimation and on the application of clustering algorithms in order to reveal the patterns of spatial population distribution. The notion “unsupervised” also means, that only some general criteria—density, dimensionality, homogeneity, are used to construct an input feature space, without adding any supervised/expert knowledge. The approach is very powerful and provides a comprehensive local information about density and homogeneity/fractality of spatially distributed point patterns.

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