Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

Do Thanh Huong; Nguyen Thi Hang Duy; Pham Vu Minh Tu; Huu Hoang Hanh; Kou Yamada

doi:10.4108/eetsc.7286

Authors

Do Thanh Huong Gunma University
Nguyen Thi Hang Duy Gunma University
Pham Vu Minh Tu Posts and Telecommunications Institute of Technology
Huu Hoang Hanh Posts and Telecommunications Institute of Technology
Kou Yamada Gunma University

DOI:

https://doi.org/10.4108/eetsc.7286

Keywords:

LoRaWAN, IoT, AR, ML, Smart Farming, Precision Agriculture

Abstract

Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.

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Author Biographies

Do Thanh Huong, Gunma University

Other affiliations: Posts and Telecommunications Institute of Technology (PTIT)

Nguyen Thi Hang Duy, Gunma University

Other affiliations: Posts and Telecommunications Institute of Technology (PTIT)

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