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Authors: Sarthak Mishra 1 ; Long Ma 2 and Nischal Aryal 2

Affiliations: 1 Department of Computer Science, University of Illinois at Urbana Champaign, Urbana, IL, U.S.A. ; 2 Department of Computer Science, Troy University, Troy, AL, U.S.A.

Keyword(s): Agriculture, Crop Production, Cotton Yield, Prediction, Regression.

Abstract: The agriculture and farming industry plays a vital role in the economy. However, the importance of agriculture cannot be fully quantified in terms of its economic profit. Agriculture affecting global hunger is a much more sensitive and vital topic. One of the leading reasons for this is un-improvised crop production. Crop production is affected by various factors, and monitoring those factors is the key to solving the problem. This paper describes a comprehensive experiment predicting the cotton yield under various environments, such as Acres Harvested, Acres Planted, Soil pH, Bulk Density, Clay-High, Clay-Low, Organic-Carbon, and Water-Area.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Mishra, S., Ma, L. and Aryal, N. (2021). Prediction of Cotton Field on Integrated Environmental Data. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 781-786. DOI: 10.5220/0010240707810786

@conference{icaart21,
author={Sarthak Mishra and Long Ma and Nischal Aryal},
title={Prediction of Cotton Field on Integrated Environmental Data},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={781-786},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010240707810786},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Prediction of Cotton Field on Integrated Environmental Data
SN - 978-989-758-484-8
IS - 2184-433X
AU - Mishra, S.
AU - Ma, L.
AU - Aryal, N.
PY - 2021
SP - 781
EP - 786
DO - 10.5220/0010240707810786
PB - SciTePress