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Automated Pattern Recognition

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J.v. Stafford – One of the best experts on this subject based on the ideXlab platform.

  • Information on within-field variability from sequences of yield maps: multivariate classification as a first step of interpretation
    Nutrient Cycling in Agroecosystems, 1998
    Co-Authors: R.m. Lark, J.v. Stafford

    Abstract:

    It is shown that Automated Pattern Recognition applied to a series of yield maps can be used to divide a field into regions within which yields show similar between-season variation. These regions are associated with particular soil types. Such a regionalisation may be a useful way of recognising important within-field scales of variability, and may be a useful first step in interpretation to develop a management response.

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  • Classification as a first step in the interpretation of temporal and spatial variation of crop yield
    Annals of Applied Biology, 1997
    Co-Authors: R.m. Lark, J.v. Stafford

    Abstract:

    Summary.

    Automated Pattern Recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general Patterns of season-to-season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.

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R.m. Lark – One of the best experts on this subject based on the ideXlab platform.

  • Information on within-field variability from sequences of yield maps: multivariate classification as a first step of interpretation
    Nutrient Cycling in Agroecosystems, 1998
    Co-Authors: R.m. Lark, J.v. Stafford

    Abstract:

    It is shown that Automated Pattern Recognition applied to a series of yield maps can be used to divide a field into regions within which yields show similar between-season variation. These regions are associated with particular soil types. Such a regionalisation may be a useful way of recognising important within-field scales of variability, and may be a useful first step in interpretation to develop a management response.

    Free Register to Access Article

  • Classification as a first step in the interpretation of temporal and spatial variation of crop yield
    Annals of Applied Biology, 1997
    Co-Authors: R.m. Lark, J.v. Stafford

    Abstract:

    Summary.

    Automated Pattern Recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general Patterns of season-to-season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.

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N Fuller – One of the best experts on this subject based on the ideXlab platform.

  • Solar feature catalogues in EGSO
    SOL PHYS, 2005
    Co-Authors: N Fuller

    Abstract:

    The Solar Feature Catalogues (SFCs) are created from digitized solar images using Automated Pattern Recognition techniques developed in the European Grid of Solar Observation (EGSO) project. The techniques were applied for detection of sunspots, active regions and filaments in the automatically standardized full-disk solar images in CaII K1, CaII K3 and H alpha taken at the Meudon Observatory and white-light images and magnetograms from SOHO/MDI. The results of Automated Recognition are verified with the manual synoptic maps and available statistical data from other observatories that revealed high detection accuracy. A structured database of the Solar Feature Catalogues is built on the MySQL server for every feature from their recognized parameters and cross-referenced to the original observations. The SFCs are published on the Bradford University web site http://www.cyber.brad.ac.uk/egso/SFC/ with the pre-designed web pages for a search by time, size and location. The SFCs with 9 year coverage (1996-2004) provide any possible information that can be extracted from full disk digital solar images. Thus information can be used for deeper investigation of the feature origin and association with other features for their Automated classification and solar activity forecast.

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  • Searchable solar feature catalogues
    Advances in Space Research, 2005
    Co-Authors: V. V. Zharkova, J. Aboudarham, S. Zharkov, Stanley S. Ipson, A. K. Benkhalil, N Fuller

    Abstract:

    Abstract The searchable Solar Feature Catalogues (SFCs) are developed from digitized solar images using Automated Pattern Recognition techniques. The techniques were applied for the detection of sunspots, active regions, filaments and line-of-sight magnetic neutral lines in automatically standardized full disk solar images in Ca II K1, Ca II K3 and Ha lines taken at the Paris-Meudon Observatory and white light images and magnetograms from SOHO/MDI. The results of the Automated Recognition were verified with manual synoptic maps and available statistical data that revealed good detection accuracy. Based on the recognized parameters, a structured database of Solar Feature Catalogues was built on a MySQL server for every feature and published with various pre-designed search pages on the Bradford University web site http://www.cyber.brad.ac.uk/egso/SFC/ . The SFCs with nine year coverage (1996–2004) is to be used for deeper investigation of the feature classification and solar activity forecast.

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  • Searchable Solar Feature Catalogues in EGSO
    , 2004
    Co-Authors: V. V. Zharkova, J. Aboudarham, S. Zharkov, Stanley S. Ipson, A. K. Benkhalil, N Fuller

    Abstract:

    This paper describes a searchable Solar Feature Catalogue (SFC) created using Automated Pattern Recognition techniques from digitized solar images. The techniques were developed for detection of sunspots, active regions, filaments and line-of-sight magnetic neutral lines using Ca II K1, Ca II K3 and Ha solar images from the Meudon Observatory and white light images and magnetograms from SOHO/MDI. A comp arison of the results of automatic detection with manually generated synoptic maps shows good detection accuracy. Using the characteristics extracted from the recognized features a structured database of the Solar Feature Catalogues has been built on a mysql server and published with various pre-designed search pages on the Bradford University web site http://www.cyber.brad.ac.uk/egso/. The future SFC with 11 year coverage (1995-2005) is to be used for feature classification and short-term and long-term solar activity forecast. This research is a part of the European Grid of Solar Observations (EGSO) project.

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