UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 12 | December 2024

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 10 Issue 1
January-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2301221


Registration ID:
506943

Page Number

c169-c176

Share This Article


Jetir RMS

Title

Environmental Temperature Prediction using IoT- based Machine Learning Technique for Agriculture

Abstract

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.

Key Words

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

Cite This Article

"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

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"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

Publication Details

Published Paper ID: JETIR2301221
Registration ID: 506943
Published In: Volume 10 | Issue 1 | Year January-2023
DOI (Digital Object Identifier):
Page No: c169-c176
Country: Dewas, Madhya Pradesh, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000154

Print This Page

Current Call For Paper

Jetir RMS