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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Studying the Impact of Water Supply on Wheat Yield by using Principle Lasso Radial Machine Learning Model

Author 1: Muhammad Adnan
Author 2: M. Abid
Author 3: M. Ahsan Latif
Author 4: Abaid-ur-Rehman
Author 5: Naheed Akhter
Author 6: Muhammad Kashif

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090232

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 2, 2018.

  • Abstract and Keywords
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Abstract: Wheat plays a vital role in the food production as it fulfills 60% requirements of calories and proteins to the 35% of the world population. Owing to wheat importance in food, wheat demand is increasing continuously. Wheat yield is committed to the availability of water supply. Due to climatic and environmental variations of different countries, water supply is not available in constant and desire quantity that is necessary for better wheat yield. So, there is a strong relationship and dependency that exists between water supply and wheat yield. Therefore, water supply is becoming an issue because it directly effects wheat yield. In this research, a Principle Lasso Radial (PLR) model is proposed using Machine Learning technique to measure the effect of water supply on wheat yield. In this Principle Lasso Radial (PLR) model, various experiments are conducted with respect to the performance metrics, i.e. relative water contents, waxiness, grain per spike and plant height. Principle Lasso Radial (PLR) model’s produced reduced dimensional data with respect to performance metrics. That data is provided to Radial Basis Neural Network (RBNN), and it showed regression values R under different water supply conditions. Principle Lasso Radial (PLR) model achieved an accuracy of 89% among variance Machine Learning techniques.

Keywords: Radial basis function (RBF); Radial Basis Neural Network (RBNN); ANN; lasso; principle component analysis (PCA)

Muhammad Adnan, M. Abid, M. Ahsan Latif, Abaid-ur-Rehman, Naheed Akhter and Muhammad Kashif, “Studying the Impact of Water Supply on Wheat Yield by using Principle Lasso Radial Machine Learning Model” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090232

@article{Adnan2018,
title = {Studying the Impact of Water Supply on Wheat Yield by using Principle Lasso Radial Machine Learning Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090232},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090232},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {2},
author = {Muhammad Adnan and M. Abid and M. Ahsan Latif and Abaid-ur-Rehman and Naheed Akhter and Muhammad Kashif}
}


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