Abstract

 

This research project aims to predict optimal humidity levels in greenhouses using machine learning models based on historical data collected on temperature and humidity. Through this project, we explore how to apply mathematical concepts such as statistics, linear regression, and algebra. 

The study compares the effectiveness of linear and polynomial regression models in predicting humidity levels. Sensors collect real time data, which is then processed using Python libraries. The results guide the automation of greenhouse systems, such as foggers, circulating fans, fan-pad system and sprinklers, ensuring precise environmental control. The integration with IoT technology enables remote monitoring and management, promoting resource efficiency and optimal plant growth conditions.

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This research paper has been published in the International Journal of Science and Research (IJSR) in August, 2024, under Volume 13, Issue 8.

The link for the same is given below:

https://www.ijsr.net/getabstract.php?paperid=SR24804174838 

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