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Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire)

Received: 23 February 2021    Accepted: 8 March 2021    Published: 26 March 2021
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Abstract

The growth of maize is a complex phenomenon which involves certain parameters including the number of leaves, the length of the leaves, the width of the leaves, the height and the circumference of the plant. A study of these growth parameters was carried out in the region of Daloa (Côte d’Ivoire). These measurements could show a complexity of the growth of maize. To this end, mathematical models have been developed to predict this growth from artificial neural networks for the number of leaves, the length of the leaves, the width of the leaves, the height of the plant and the circumference of the trunk of the maize plant. The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9914; 0.9965; 0.9872; 0.9995 and 0.9976 for plant height; the number of leaves; the circumference of the plant; leaf length and leaf width. Satisfactory results have been obtained insofar as all the coefficients of determination are greater than 0.98. These coefficients close to 1 show a good interpolation between the experimental values and those predicted by the model. Because of this, we can say that the values predicted by the artificial neural network are reliable enough to predict the growth of maize.

DOI 10.11648/j.aff.20211002.17
Published in Agriculture, Forestry and Fisheries (Volume 10, Issue 2, April 2021)
Page(s) 85-92
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), 2024. Published by Science Publishing Group

Keywords

Maize, Growth Parameter, Modeling, Artificial Neural Network

References
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    Kouame N’Guessan, Assidjo Nogbou Emmanuel. (2021). Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire). Agriculture, Forestry and Fisheries, 10(2), 85-92. https://doi.org/10.11648/j.aff.20211002.17

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    ACS Style

    Kouame N’Guessan; Assidjo Nogbou Emmanuel. Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire). Agric. For. Fish. 2021, 10(2), 85-92. doi: 10.11648/j.aff.20211002.17

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    AMA Style

    Kouame N’Guessan, Assidjo Nogbou Emmanuel. Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire). Agric For Fish. 2021;10(2):85-92. doi: 10.11648/j.aff.20211002.17

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  • @article{10.11648/j.aff.20211002.17,
      author = {Kouame N’Guessan and Assidjo Nogbou Emmanuel},
      title = {Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire)},
      journal = {Agriculture, Forestry and Fisheries},
      volume = {10},
      number = {2},
      pages = {85-92},
      doi = {10.11648/j.aff.20211002.17},
      url = {https://doi.org/10.11648/j.aff.20211002.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20211002.17},
      abstract = {The growth of maize is a complex phenomenon which involves certain parameters including the number of leaves, the length of the leaves, the width of the leaves, the height and the circumference of the plant. A study of these growth parameters was carried out in the region of Daloa (Côte d’Ivoire). These measurements could show a complexity of the growth of maize. To this end, mathematical models have been developed to predict this growth from artificial neural networks for the number of leaves, the length of the leaves, the width of the leaves, the height of the plant and the circumference of the trunk of the maize plant. The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9914; 0.9965; 0.9872; 0.9995 and 0.9976 for plant height; the number of leaves; the circumference of the plant; leaf length and leaf width. Satisfactory results have been obtained insofar as all the coefficients of determination are greater than 0.98. These coefficients close to 1 show a good interpolation between the experimental values and those predicted by the model. Because of this, we can say that the values predicted by the artificial neural network are reliable enough to predict the growth of maize.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Maize Growth (Zea mays l.) Modeling Using the Artificial Neural Networks Method at Daloa (Côte d’Ivoire)
    AU  - Kouame N’Guessan
    AU  - Assidjo Nogbou Emmanuel
    Y1  - 2021/03/26
    PY  - 2021
    N1  - https://doi.org/10.11648/j.aff.20211002.17
    DO  - 10.11648/j.aff.20211002.17
    T2  - Agriculture, Forestry and Fisheries
    JF  - Agriculture, Forestry and Fisheries
    JO  - Agriculture, Forestry and Fisheries
    SP  - 85
    EP  - 92
    PB  - Science Publishing Group
    SN  - 2328-5648
    UR  - https://doi.org/10.11648/j.aff.20211002.17
    AB  - The growth of maize is a complex phenomenon which involves certain parameters including the number of leaves, the length of the leaves, the width of the leaves, the height and the circumference of the plant. A study of these growth parameters was carried out in the region of Daloa (Côte d’Ivoire). These measurements could show a complexity of the growth of maize. To this end, mathematical models have been developed to predict this growth from artificial neural networks for the number of leaves, the length of the leaves, the width of the leaves, the height of the plant and the circumference of the trunk of the maize plant. The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9914; 0.9965; 0.9872; 0.9995 and 0.9976 for plant height; the number of leaves; the circumference of the plant; leaf length and leaf width. Satisfactory results have been obtained insofar as all the coefficients of determination are greater than 0.98. These coefficients close to 1 show a good interpolation between the experimental values and those predicted by the model. Because of this, we can say that the values predicted by the artificial neural network are reliable enough to predict the growth of maize.
    VL  - 10
    IS  - 2
    ER  - 

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Author Information
  • Agricultural Production Improvement Laboratory, University Jean Lorougnon Guede, Agroforestry Training and Research Unit, Daloa, C?te d’Ivoire

  • Laboratory of Industrial Synthesis and Environment Processes, National Polytechnic Institute Felix Houphou?t-Boigny, Yamoussoukro, C?te d’Ivoire

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