The rapid advancement of Internet of Things (IoT) technologies presents significant opportunities to enhance agriculture and aquaculture practices. This paper proposed a software architecture designed based on microservices to address the complex and evolving requirements of contemporary agriculture and aquaculture systems locations. These systems demand a versatile platform capable of integrating a wide range of functionalities, including environmental monitoring, aquaculture management, and comprehensive record-keeping for crops and aquatic life. The proposed IoT system employs a microservice architecture that ensures modularity, scalability, and flexibility. This approach facilitates the seamless integration of new services and features, easy adaptation to existing systems, and efficient reuse of established components. The microservice-based architecture has been successfully implemented in the TC2 project at Can Tho University under the Japan International Cooperation Agency (JICA) initiative. The project is in remote areas through the deployment of a network of sensor and actuator nodes, supported by a robust backend and user-friendly Front End applications. This implementation demonstrated notable improvements in operational efficiency and resource management. By leveraging edge computing for real-time data processing and integrating cloud-based analytics, the system achieved reduced response times and minimized data transmission costs. The modularity of the microservices architecture also enabled easy scalability and adaptability to change environmental conditions. The success of this implementation highlights the potential of microservices-based IoT solutions to drive innovation and enhance practices in agriculture and aquaculture, laying a strong foundation for future advancements in these fields.

Latency and connectivity problems in traditional cloud computing platforms regularly hinder the use of Internet of Things (IoT) systems for smart agriculture, especially in remote rural areas. Network quality is an essential element of centralized architectures since they require continuous telemetry transmission to support real-time control decisions. This sensitivity leads to delayed control responses and possible over-control of high-capacity farm machinery, raising operating expenses. To solve these problems, this paper proposes a lightweight edge computing architecture based on embedded microservices for fast, reliable, and automated control. The thresholds for different environmental conditions are set directly on the ESP32 microcontroller, and a wireless sensor network is used to create a fully local control loop. This configuration enables edge devices to autonomously and accurately recognize anomalies in environmental conditions at the monitoring site, eliminating the need to await cloud server processing. The experimental system was built to demonstrate the benefits of on-premises processing over cloud-based processing by carefully measuring trigger latency and fault tolerance under different network conditions in rural areas. The results indicate that the Edge architecture reliably lowers network round-trip latency, reducing overall trigger latency by 40.09% compared to the cloud platform. Without network-related trigger delays, the system lets local hardware make control decisions quickly and reliably, even if the network goes down completely. Additionally, the architecture substantially decreases unnecessary energy consumption by heavy agricultural actuators, such as aeration pumps used in aquaculture farms. In summary, this decoupled architecture improves real-time response and energy efficiency. It also makes it easier to add fog computing and distributed artificial intelligence to smart agriculture systems.