Year: 2025
Type: article
Abstract: This study proposes a cloud-based precision agriculture management system to address the limitations of conventional smart farming environments that rely on on-premise devices and localized processing.These legacy systems face challenges in scalability, interoperability, and real-time responsiveness, making it difficult to manage crop growth, environmental control, pest detection, and farming operations in an integrated manner.To overcome these issues, we designed a multi-layered architecture based on the Farm as a Service (FaaS) model, composed of Edge-IoT, Gateway, and Cloud layers.The system consists of six modular services-EMS, DMS, MMS, FMS, FCS, and FOS-each developed in compliance with TTAK standards to ensure interoperability and scalability.Edge Agents serve as the field interface for real-time data collection and control signal delivery between on-site devices and the cloud.The system was deployed in a strawberry greenhouse environment, where core features such as agent registration, controller integration, service installation, user interface interaction, and automated control feedback were successfully implemented.This structure aligned precisely with the system's design model.The platform is suitable for multi-farm expansion and AI-based autonomous crop management.Future enhancements will include image-based disease diagnostics, public API integration, and cross-platform data synchronization to increase adaptability and effectiveness in smart farming practices.
Authors: Seung Jae Kim, Meong Hun Lee
Institution: Sunchon National University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4413835489