DOI : https://doi.org/10.18517/ijaseit.14.1.18745

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono (1), Fatma Ayu Nuning Farida Afiatna (2), Nur Muflihah (3)
(1) Department of Industrial Engineering, University of Hasyim Asy’ari Tebuireng Jombang, Jombang, East Java, 61471, Indonesia
(2) Department of Industrial Engineering, University of Hasyim Asy’ari Tebuireng Jombang, Jombang, East Java, 61471, Indonesia
(3) Department of Industrial Engineering, University of Hasyim Asy’ari Tebuireng Jombang, Jombang, East Java, 61471, Indonesia
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How to cite (IJASEIT) :
Sumarsono, et al. “The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 1, Feb. 2024, pp. 118-30, doi:10.18517/ijaseit.14.1.18745.
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In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

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