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Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images

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Artificial Intelligence Perspectives and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 347))

Abstract

Agriculture department plays vital role on forecasting crop production and acreage estimation in the State. Commodity market estimates crop production on the basis of crop mass arrival in market and field prediction from authorized sources like Crop Advisory Boards. However, it is obvious that estimates from such board and government are often remains unmatched due to non-qualitative and unreliable approaches. The timely and accurate acreage estimation of crop is the pre-requisite for the purpose of better management upon crop production estimation. The conventional methods of gathering information on crop acreage are cumbersome, costly, and protracted, especially when the extent of work is whole county. The crop acreage statistics proves more crucial in event of natural calamity for taking strategic decisions like compensations to farmers based on losses they come up with. In a nutshell, non-availibity of accurate and finely estimated forecast necessitates the formation of coherent policy on fixing up agricultural commodity prices. Finally, soft classification approaches proved to be an alternative to error prone crop statistics by virtue of machine learning algorithms that applied on remote sensing images, a third eye technology which never lies. This paper conferred about gamut of machine learning algorithms for satellite data applications and envisages future trends that would be a magnet for researchers in upcoming years.

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Author information

Authors and Affiliations

  1. Maharashtra Remote Sensing Applications Centre, Nagpur, India

    Anand N. Khobragade

  2. Computer Technology Department, YC College of Engineering, Nagpur, India

    Mukesh M. Raghuwanshi

Authors
  1. Anand N. Khobragade
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  2. Mukesh M. Raghuwanshi
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Corresponding author

Correspondence to Anand N. Khobragade .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zuzana Kominkova Oplatkova

  4. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

  5. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

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Khobragade, A.N., Raghuwanshi, M.M. (2015). Contextual Soft Classification Approaches for Crops Identification Using Multi-sensory Remote Sensing Data: Machine Learning Perspective for Satellite Images. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Perspectives and Applications. Advances in Intelligent Systems and Computing, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-319-18476-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-18476-0_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18475-3

  • Online ISBN: 978-3-319-18476-0

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