Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR - Huong, Do Thanh; Duy, Nguyen Thi Hang; Tu, Pham Vu Minh; Hanh, Huu Hoang; Yamada, Kou (EAI Endorsed Transactions on Smart Cities, 2024)
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.