Skip to Content
ProceedingsProceedings
  • Abstract
  • Open Access

28 May 2024

EcoSense: A Smart IoT-Based Digital Twin Monitoring System for Enhanced Farm Climate Insights †

Scientific Computing, Computer Science and Data Science Research Unit (CSIDS), Faculty of Sciences and Techniques, University of Nouakchott, Nouakchott P.O. Box 880, Mauritania
Presented at the 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering (ECP 2024), 29–31 May 2024; Available online: https://sciforum.net/event/ECP2024.
The convergence of digital technology and agriculture has given rise to innovative solutions aimed at augmenting productivity, sustainability, and efficiency in agriculture. One transformative idea that has garnered significant attention is the “Digital Twin” (DT), transcending boundaries and finding applications in various sectors, including agriculture. This contribution introduces the design and implementation of a Smart IoT-Based Digital Twin Monitoring System that gathers real-time Total Volatile Organic Compounds (TVOC), signifying the total concentration of volatile organic compounds present in the air, contributing to indoor air pollution. Equivalent Carbon Dioxide (eCO2) provides an indication of the amount of CO2 in the air. Noise pertains to unwanted or disruptive sound in the environment. Humidity denotes the amount of water vapor present in the air. Temperature is a measure of the warmth or coldness of the air, water, or any substance. Its monitoring is crucial in various applications, including climate studies, weather forecasting, and industrial processes. Air Quality pertains to the state of the air in the environment concerning the presence of pollutants, particulate matter, and other substances. Its monitoring is essential for public health and environmental protection. The collected data are sent to the cloud for storage and analysis. This climate information is indispensable for the farmer to make informed decisions at the opportune moment, take actions based on the gathered data, and predict crop yield. Our intelligent system has the potential, in the future, to make decisions and manage automatic irrigation using solenoid valves based on the real-time collected data.

Supplementary Materials

The presentation materials can be downloaded at: https://www.mdpi.com/article/10.3390/proceedings2024105016/s1.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data will be the subject of a separate publication in a data paper journal. We plan to publish the first Mauritanian agricultural database for other researchers to use in their future research. Therefore, the data are not yet publicly available.

Conflicts of Interest

The author declares no conflict of interest.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.