Precision Agriculture Using Internet of Things and Wireless Sensor Networks

Saha, Pinaki; Kumar, Vikas; Kathuria, Samta; Gehlot, Anita; Pachouri, Vikrant; Duggal, Angel Swastik

doi:10.1109/icdt57929.2023.10150678

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…It has not always been successful. Because of the negative consequences on the ecosystem [5]. These few instances highlight the limitations of agricultural management using manual technology, which cannot deliver precise processes and outcomes.…”

Section: Introductionmentioning

confidence: 99%

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

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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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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Section: Introductionmentioning

confidence: 99%

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

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Machine learning (ML) has the potential to help the agriculture industry overcome it`s problems [2]. In order to increase crop productivity, forecast plant disease, and meet consumer demand, research is being done to leverage machine learning and a data-centric strategy in agriculture [3].…”

Section: Introductionmentioning

confidence: 99%