Using Prescriptive Analytics for the Determination of Optimal Crop Yield

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Malik Adeiza Rufai; Oluoha Onyekwere; Alao Abiodun Abdulrahman; Idris Abdulkadri

doi:doi:10.11648/j.ijdsa.20200603.11

Using Prescriptive Analytics for the Determination of Optimal Crop Yield

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, Idris Abdulkadri

Published in International Journal of Data Science and Analysis (Volume 6, Issue 3)

Received: 28 May 2020 Accepted: 8 June 2020 Published: 6 July 2020

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Abstract

The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.

Published in	International Journal of Data Science and Analysis (Volume 6, Issue 3)
DOI	10.11648/j.ijdsa.20200603.11
Page(s)	72-82
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Prescriptive Analytics, Optimal, Crop Yield, Machine Learning, Support Vector Machine, XGBoost

References

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APA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, et al. (2020). Using Prescriptive Analytics for the Determination of Optimal Crop Yield. International Journal of Data Science and Analysis, 6(3), 72-82. https://doi.org/10.11648/j.ijdsa.20200603.11

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ACS Style

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Malik Adeiza Rufai; Oluoha Onyekwere; Alao Abiodun Abdulrahman, et al. Using Prescriptive Analytics for the Determination of Optimal Crop Yield. Int. J. Data Sci. Anal. 2020, 6(3), 72-82. doi: 10.11648/j.ijdsa.20200603.11

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AMA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, et al. Using Prescriptive Analytics for the Determination of Optimal Crop Yield. Int J Data Sci Anal. 2020;6(3):72-82. doi: 10.11648/j.ijdsa.20200603.11

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@article{10.11648/j.ijdsa.20200603.11,
  author = {Terungwa Simon Yange and Charity Ojochogwu Egbunu and Malik Adeiza Rufai and Oluoha Onyekwere and Alao Abiodun Abdulrahman and Idris Abdulkadri},
  title = {Using Prescriptive Analytics for the Determination of Optimal Crop Yield},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {3},
  pages = {72-82},
  doi = {10.11648/j.ijdsa.20200603.11},
  url = {https://doi.org/10.11648/j.ijdsa.20200603.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200603.11},
  abstract = {The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.},
 year = {2020}
}

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TY  - JOUR
T1  - Using Prescriptive Analytics for the Determination of Optimal Crop Yield
AU  - Terungwa Simon Yange
AU  - Charity Ojochogwu Egbunu
AU  - Malik Adeiza Rufai
AU  - Oluoha Onyekwere
AU  - Alao Abiodun Abdulrahman
AU  - Idris Abdulkadri
Y1  - 2020/07/06
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200603.11
DO  - 10.11648/j.ijdsa.20200603.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 72
EP  - 82
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200603.11
AB  - The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.
VL  - 6
IS  - 3
ER  -

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Author Information

Terungwa Simon Yange

Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Charity Ojochogwu Egbunu

Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria
Malik Adeiza Rufai

Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Oluoha Onyekwere

Department of Computer Science, University of Nigeria, Nsukka, Nigeria
Alao Abiodun Abdulrahman

Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria
Idris Abdulkadri

Department of Computer Science, Federal University Lokoja, Lokoja, Nigeria

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APA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, et al. (2020). Using Prescriptive Analytics for the Determination of Optimal Crop Yield. International Journal of Data Science and Analysis, 6(3), 72-82. https://doi.org/10.11648/j.ijdsa.20200603.11

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ACS Style

Terungwa Simon Yange; Charity Ojochogwu Egbunu; Malik Adeiza Rufai; Oluoha Onyekwere; Alao Abiodun Abdulrahman, et al. Using Prescriptive Analytics for the Determination of Optimal Crop Yield. Int. J. Data Sci. Anal. 2020, 6(3), 72-82. doi: 10.11648/j.ijdsa.20200603.11

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AMA Style

Terungwa Simon Yange, Charity Ojochogwu Egbunu, Malik Adeiza Rufai, Oluoha Onyekwere, Alao Abiodun Abdulrahman, et al. Using Prescriptive Analytics for the Determination of Optimal Crop Yield. Int J Data Sci Anal. 2020;6(3):72-82. doi: 10.11648/j.ijdsa.20200603.11

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@article{10.11648/j.ijdsa.20200603.11,
  author = {Terungwa Simon Yange and Charity Ojochogwu Egbunu and Malik Adeiza Rufai and Oluoha Onyekwere and Alao Abiodun Abdulrahman and Idris Abdulkadri},
  title = {Using Prescriptive Analytics for the Determination of Optimal Crop Yield},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {3},
  pages = {72-82},
  doi = {10.11648/j.ijdsa.20200603.11},
  url = {https://doi.org/10.11648/j.ijdsa.20200603.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200603.11},
  abstract = {The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.},
 year = {2020}
}

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TY  - JOUR
T1  - Using Prescriptive Analytics for the Determination of Optimal Crop Yield
AU  - Terungwa Simon Yange
AU  - Charity Ojochogwu Egbunu
AU  - Malik Adeiza Rufai
AU  - Oluoha Onyekwere
AU  - Alao Abiodun Abdulrahman
AU  - Idris Abdulkadri
Y1  - 2020/07/06
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdsa.20200603.11
DO  - 10.11648/j.ijdsa.20200603.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 72
EP  - 82
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20200603.11
AB  - The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.
VL  - 6
IS  - 3
ER  -

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