Research Article
Smart Weather Monitoring for Coffee Plantations: IoT-Based Automated Data Logging and Agronomic Decision Support
@INPROCEEDINGS{10.4108/eai.16-9-2025.2361000, author={Nurul Maulida Surbakti and Muhammad Ashari and Dinda Kartika and Zul Amry and Riza Pahlawan}, title={Smart Weather Monitoring for Coffee Plantations: IoT-Based Automated Data Logging and Agronomic Decision Support}, proceedings={Proceedings of the 7th International Conference on Innovation in Education, Science, and Culture, ICIESC 2025, 16 September 2025, Medan, Indonesia}, publisher={EAI}, proceedings_a={ICIESC}, year={2026}, month={3}, keywords={iot automated weather station agriculture data logging decision support}, doi={10.4108/eai.16-9-2025.2361000} }- Nurul Maulida Surbakti
Muhammad Ashari
Dinda Kartika
Zul Amry
Riza Pahlawan
Year: 2026
Smart Weather Monitoring for Coffee Plantations: IoT-Based Automated Data Logging and Agronomic Decision Support
ICIESC
EAI
DOI: 10.4108/eai.16-9-2025.2361000
Abstract
Weather information is a crucial factor in supporting agricultural activities, particularly in tropical regions like Indonesia, which frequently face climate variability and extreme weather phenomena. This research aims to design and implement an Internet of Things (IoT)-based Automated Weather Station (AWS) for a coffee plantation in Perteguhan Village, Simpang Empat District. The system uses an ESP32 microcontroller and a SIM7600A communication module to collect real-time weather data, including air temperature, relative humidity, and wind speed and direction. Data is sent via the MQTT protocol to a cloud database (Google Sheets) and displayed via a Kodular-based mobile application, which is also integrated with weather forecast data from the Meteorology, Climatology, and Geophysics Agency (BMKG). In addition to monitoring, the system is equipped with a decision tree model that processes a combination of sensor data and literature to generate agronomic recommendations. A one-month field trial demonstrated stable system performance with minimal data loss, and measurement results were consistent with secondary data from the BMKG API. The results of this study indicate that the developed IoT-based AWS is practical, affordable, and scalable, thus having the potential to support agronomic decision-making, increase farmers' resilience to climate variability, and strengthen sustainable agricultural practices in rural communities.
