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International Conference on Artificial Intelligence and Data Engineering

AIDE 2019: Advances in Artificial Intelligence and Data Engineering pp 1273–1280Cite as

Smart Agro-Ecological Zoning for Crop Suggestion and Prediction Using Machine Learning: An Comprehensive Review

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1133))

Abstract

Crop production in agriculture depends on many factors such as climate, geography, biological, economical, historical, political, socioeconomic and agro-ecological zoning. Intelligent agro-ecological zoning is at the forefront of this, the main aim is accurately suggesting and prediction of crops that ensure more production of it. This paper is a review for reassessing the research work on the agricultural crop suggestion and prediction with relevance to machine learning techniques to find research gap and to provide future research direction.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, JSS Academy of Technical Education, VTU, Bengaluru, India

    R. Chetan

  2. Department of Information Science and Engineering, JSS Academy of Technical Education, VTU, Bengaluru, India

    D. V. Ashoka

  3. Department of Computer Science and Engineering, SJB Institute of Technology, VTU, Bengaluru, India

    B. V. Ajay Prakash

Authors
  1. R. Chetan
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  2. D. V. Ashoka
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  3. B. V. Ajay Prakash
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Corresponding author

Correspondence to R. Chetan .

Editor information

Editors and Affiliations

  1. NMAM Institute of Technology, Udupi, India

    Niranjan N. Chiplunkar

  2. Ritsumeikan University, Shiga, Japan

    Takanori Fukao

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Cite this paper

Chetan, R., Ashoka, D.V., Ajay Prakash, B.V. (2021). Smart Agro-Ecological Zoning for Crop Suggestion and Prediction Using Machine Learning: An Comprehensive Review. In: Chiplunkar, N.N., Fukao, T. (eds) Advances in Artificial Intelligence and Data Engineering. AIDE 2019. Advances in Intelligent Systems and Computing, vol 1133. Springer, Singapore. https://doi.org/10.1007/978-981-15-3514-7_94

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