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research-article

Delineation of management zones in an apple orchard in Greece using a multivariate approach

Authors:

Katerina Aggelopooulou,

Annamaria Castrignanò,

Theofanis Gemtos,

Daniela De BenedettoAuthors Info & Claims

Computers and Electronics in Agriculture, Volume 90, Issue C

Pages 119 - 130

Published: 01 January 2013 Publication History

Abstract

Highlights High spatial variability of soil, yield and quality in apples. High temporal variability over the 3years of the experiment. Management zones created using multivariate geostatistics. Management zone (MZ) delineation is important for the application of Precision Agriculture because farm management decisions are based on it. Several factors were used for the MZ delineation including crop and soil characteristics. In the present paper multivariate analysis was applied to delineate MZs. Soil and crop data, collected over 3years from a Precision Agriculture project in an apple orchard in Greece, were used. The collected data were categorized in three groups, namely soil properties, yield and fruit quality. All data were analyzed for descriptive statistics and their distribution. Maps of the spatial variability for the 3years were presented. Data were jointly analyzed for management zone delineation using a combination of multivariate geostatistics with a non-parametric clustering approach, and the orchard was divided in four zones which could be differently managed. However, further research and experimentation are needed before precision horticulture being confidently adopted in Greece.

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Cited By

Chang DZhang JZhu LGe SLi PLiu G(2014)Delineation of management zones using an active canopy sensor for a tobacco fieldComputers and Electronics in Agriculture10.1016/j.compag.2014.09.019109:C(172-178)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1016/j.compag.2014.09.019

Delineation of management zones in an apple orchard in Greece using a multivariate approach
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 90, Issue C

January 2013

192 pages

ISSN:0168-1699

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2013

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Cited By

Chang DZhang JZhu LGe SLi PLiu G(2014)Delineation of management zones using an active canopy sensor for a tobacco fieldComputers and Electronics in Agriculture10.1016/j.compag.2014.09.019109:C(172-178)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1016/j.compag.2014.09.019

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