Abstract:
This paper offers a detailed investigation into poultry health monitoring utilizing an
Internet of Things (IoT) platform and Machine Learning (ML) approaches. This article
offers a detailed investigation into poultry health monitoring utilizing an Internet of
Things (IoT) platform and Machine Learning (ML) approaches. The rising availability of
low-cost processing resources, IoT sensors, and standard algorithms has made it
appealing to use current technology to continually monitor big farms with millions of
birds and enhance overall production. This model collects temperature, humidity, gasses,
luminosity, and wind speed data and stores them in a cloud database. Using the dataset,
the machine learning classification algorithms Nave Bias (NB), Support Vector Machine
(SVM), and Random Forest (RF) perform and predict poultry health properly. This model
predicts the best option for the birds' comfort throughout their life cycle and can use IoT
to control lights, fans, sprinklers, and curtains automatically. By applying modern
technologies in the poultry farm it can reduce bird problems like contagious diseases and
identify more quickly. As a result, it yields better-quality poultry meat and eggs. Also,
poultry farm diseases are communicable and contagious, this automation system protects
the health of human workers.
