Inside Greenhouse Climatic Prediction at North Coastal West Java

DOI

10.2991/978-94-6463-587-4_78How to use a DOI?

Keywords

Greenhouse Climate Prediction; Historical Data Analysis; Model Validation; Sustainable Agriculture; Temperature and Humidity Modeling

Abstract

This study aims to develop an accurate and reliable model of greenhouse climate conditions. This research focuses on analyzing the complex interactions between temperature, humidity, light intensity, and airflow patterns in a greenhouse environment. Historical data analysis is used to create the model. This methodology involves collecting data from various websites that provide information on parameters such as air temperature, airspeed, and sunlight intensity. Data collection is used to predict temperature and humidity parameters in the greenhouse. Model validation is done by comparing its predictions with real-time sensor data collected. The results show a high degree of accuracy, with the model successfully predicting the temperature and humidity in the greenhouse. These predictions allow farmers to make informed decisions regarding climate control systems, optimize growing conditions, and maximize crop yields. Overall, this research provides a comprehensive framework for greenhouse climate prediction, offering valuable insights into the development of efficient and sustainable growing practices. The results obtained from the model simulation reveal that the temperature inside the greenhouse consistently surpasses the air temperature outside, indicating a more controlled and warmer environment. On the other hand, the simulation also shows that the relative humidity within the greenhouse is notably lower compared to the relative humidity in the external atmosphere. This difference highlights the greenhouse’s ability to create a more tailored microclimate that can be adjusted according to the specific needs of the plants being cultivated, thereby optimizing growing conditions.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Karsid Karsid
AU  - Jauharotul Maknunah
PY  - 2024
DA  - 2024/12/01
TI  - Inside Greenhouse Climatic Prediction at North Coastal West Java
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2024 (ICoSTAS-EAS 2024)
PB  - Atlantis Press
SP  - 700
EP  - 710
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-587-4_78
DO  - 10.2991/978-94-6463-587-4_78
ID  - Karsid2024
ER  -