Work

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

Year: 2024

Type: article

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.

Source: EAI Endorsed Transactions on Smart Cities

DOI: 10.4108/eetsc.7286

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

Institutions: Gunma University, Posts and Telecommunications Institute of Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

OpenAlex ID: W4404547875

FWCI: 1.788

Cites: 16

Cited by: 8

Related to: 10