• Honey Khan
• Dr. UBS Chandrawat

To forecast the dependent variables, which include temperatures, a variety of factors must be considered. These criteria are dynamic and fluctuate over time with the atmosphere. An accurate prediction of greenhouse temperatures is critical to practical greenhouse farming. Think to speak, and technology is a rapidly evolving field that aims to integrate "matters" people and machines onto the internet. Global industrial and computer stations threaten to seriously degrade the environment because of their inherent destructive potential. Choosing the best air is a need of the highest order. Because of the alarming statistics on greenhouse gas emissions, there has been an increasing worry about the need to improve the energy efficiency of the construction sector throughout the world. It is widely accepted that building energy systems management is an essential part of the process. The ability to precisely predict the temperature within a structure is a crucial component of these systems management techniques. Regarding affecting people's quality of life, temperature and humidity are the most evident environmental indicators that can be discovered in a home—household activities to increase the general degree of intellect. Because of the increasing research on the Internet of Things and Machine Learning, many distinct models for predicting temperature have evolved. The difficulty in precisely forecasting the weather persists, despite these efforts. In this paper, we have to use the proposed LSTM method and compare it to the proposed result in the RNN model. The best technique for temperature is LSTM, with a mean absolute error of 0.8137 degrees.

Temperature Prediction, IoT, Machine Learning, RNN, LSTM.

"Environmental Temperature Prediction using IoT- based Machine Learning Technique for Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.c169-c176, January-2023, Available :http://www.jetir.org/papers/JETIR2301221.pdf

"Environmental Temperature Prediction using IoT- based Machine Learning Technique for Agriculture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppc169-c176, January-2023, Available at : http://www.jetir.org/papers/JETIR2301221.pdf