A K-Nearest Neighbor (KNN)-Algorithm in Poultry Diseases Monitoring System

Authors

  • Murtala Musa Department of Computer Science, Federal University Gusau, Nigeria.
  • Abubakar Usman Mohammed Federal University Gusau, Nigeria.
  • Samaila Musa Federal University Gusau, Nigeria.
  • Lawal Muhammad Jabaka Federal University Gusau, Nigeria.

DOI:

https://doi.org/10.57233/ijsgs.v10i3.696

Keywords:

LBP, CBN, Agriculture, Loan, Facial Recognition

Abstract

In poultry farming, the most important factor during their growth and production is for the birds to be free from any disease. Unmonitored and uncontrolled temperature and humidity within the cage can lead to reduced productivity and high rate of mortality. Diseases such as: avian influenza, Newcastle, fowl paralysis, semolina and so on may wipe up an entire farm in few minutes. This research used K- nearest neighbor (KNN) algorithm and developed a model to monitor such factors automatically. In the model, digital camera was deployed in the cage that records the behavior of the fowls. The model was trained on the normal behavior of a healthy/unhealthy fowl based on the clinical signs so that any abnormal sign reported immediately by the system. The experimental results show that the model can report the various diseases sign in poultry farms.

Author Biographies

Murtala Musa, Department of Computer Science, Federal University Gusau, Nigeria.

Department of Computer Science,

Federal University Gusau, Nigeria.

Abubakar Usman Mohammed, Federal University Gusau, Nigeria.

Department of Computer Science,

Federal University Gusau, Nigeria.

Samaila Musa, Federal University Gusau, Nigeria.

Department of Computer Science,

Federal University Gusau, Nigeria.

Lawal Muhammad Jabaka, Federal University Gusau, Nigeria.

Department of Computer Science,

Federal University Gusau, Nigeria.

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Published

2024-10-16

How to Cite

Musa, M., Mohammed, A. U. ., Musa, S. ., & Jabaka, L. M. . (2024). A K-Nearest Neighbor (KNN)-Algorithm in Poultry Diseases Monitoring System. International Journal of Science for Global Sustainability, 10(3), 47–56. https://doi.org/10.57233/ijsgs.v10i3.696