Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Ayuba Peter; Luhutyit Peter Damuut; Sa’adatu Abdulkadir

doi:doi:10.11648/j.ajnna.20200601.11

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Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques

Ayuba Peter, Luhutyit Peter Damuut, Sa’adatu Abdulkadir

Published in American Journal of Neural Networks and Applications (Volume 6, Issue 1)

Received: 28 January 2020 Accepted: 14 February 2020 Published: 26 February 2020

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Abstract

In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

Published in	American Journal of Neural Networks and Applications (Volume 6, Issue 1)
DOI	10.11648/j.ajnna.20200601.11
Page(s)	1-9
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

Artificial Neural Networks, Computational Intelligence, Convolutional Neural Networks, Maize, Optimum Maturity, Resnet50

References

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APA Style

Ayuba Peter, Luhutyit Peter Damuut, Sa’adatu Abdulkadir. (2020). Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. American Journal of Neural Networks and Applications, 6(1), 1-9. https://doi.org/10.11648/j.ajnna.20200601.11

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ACS Style

Ayuba Peter; Luhutyit Peter Damuut; Sa’adatu Abdulkadir. Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. Am. J. Neural Netw. Appl. 2020, 6(1), 1-9. doi: 10.11648/j.ajnna.20200601.11

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AMA Style

Ayuba Peter, Luhutyit Peter Damuut, Sa’adatu Abdulkadir. Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. Am J Neural Netw Appl. 2020;6(1):1-9. doi: 10.11648/j.ajnna.20200601.11

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@article{10.11648/j.ajnna.20200601.11,
  author = {Ayuba Peter and Luhutyit Peter Damuut and Sa’adatu Abdulkadir},
  title = {Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques},
  journal = {American Journal of Neural Networks and Applications},
  volume = {6},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajnna.20200601.11},
  url = {https://doi.org/10.11648/j.ajnna.20200601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20200601.11},
  abstract = {In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.},
 year = {2020}
}

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TY  - JOUR
T1  - Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques
AU  - Ayuba Peter
AU  - Luhutyit Peter Damuut
AU  - Sa’adatu Abdulkadir
Y1  - 2020/02/26
PY  - 2020
N1  - https://doi.org/10.11648/j.ajnna.20200601.11
DO  - 10.11648/j.ajnna.20200601.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20200601.11
AB  - In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.
VL  - 6
IS  - 1
ER  -

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Author Information

Ayuba Peter

Department of Mathematical Sciences, Kaduna State University, Kaduna, Nigeria
Luhutyit Peter Damuut

Department of Computer Science, Kaduna State University, Kaduna, Nigeria
Sa’adatu Abdulkadir

Department of Computer Science, Kaduna State University, Kaduna, Nigeria

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APA Style

Ayuba Peter, Luhutyit Peter Damuut, Sa’adatu Abdulkadir. (2020). Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. American Journal of Neural Networks and Applications, 6(1), 1-9. https://doi.org/10.11648/j.ajnna.20200601.11

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ACS Style

Ayuba Peter; Luhutyit Peter Damuut; Sa’adatu Abdulkadir. Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. Am. J. Neural Netw. Appl. 2020, 6(1), 1-9. doi: 10.11648/j.ajnna.20200601.11

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AMA Style

Ayuba Peter, Luhutyit Peter Damuut, Sa’adatu Abdulkadir. Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques. Am J Neural Netw Appl. 2020;6(1):1-9. doi: 10.11648/j.ajnna.20200601.11

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@article{10.11648/j.ajnna.20200601.11,
  author = {Ayuba Peter and Luhutyit Peter Damuut and Sa’adatu Abdulkadir},
  title = {Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques},
  journal = {American Journal of Neural Networks and Applications},
  volume = {6},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajnna.20200601.11},
  url = {https://doi.org/10.11648/j.ajnna.20200601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20200601.11},
  abstract = {In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Determining the Optimum Maturity of Maize Using Computational Intelligence Techniques
AU  - Ayuba Peter
AU  - Luhutyit Peter Damuut
AU  - Sa’adatu Abdulkadir
Y1  - 2020/02/26
PY  - 2020
N1  - https://doi.org/10.11648/j.ajnna.20200601.11
DO  - 10.11648/j.ajnna.20200601.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20200601.11
AB  - In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.
VL  - 6
IS  - 1
ER  -

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