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DOI: 10.14569/IJACSA.2022.0131137
PDF

Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River

Author 1: Deepika
Author 2: Nagarathna
Author 3: Channegowda

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 11, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Indian poultry industry has evolved from a simple backyard occupation to a large commercial agri-based enterprise. Chicken dominates poultry production in India, accounting for almost 95% of total egg production. Several factors affect the egg production such as feeding material, drinking water, environmental factors etc. Analyzing the water quality is one of the important tasks. Cauvery River is considered as the study area because of its importance in several states of South India which have significant contribution in poultry farming. The aim of the proposed study is to develop an automated approach of water quality analysis and present a novel machine learning approach which considers an improved feature ranking method and ensemble tree classifier with majority voting. The experimental result shows that proposed approach performs better with an accuracy of 95.12%.

Keywords: Water quality; poultry; machine learning; Cauvery river; feature ranking; ensemble tree classifier; accuracy

Deepika , Nagarathna and Channegowda, “Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River” International Journal of Advanced Computer Science and Applications(IJACSA), 13(11), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131137

@article{2022,
title = {Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131137},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131137},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {11},
author = {Deepika and Nagarathna and Channegowda}
}



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.

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