# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 4 Methodology
### Network Architecture  
The proposed network architecture comprises a certain number \(G\) of greenhouses, each containing a series of extreme edge devices for sensing and actuation. This component is represented on the left side of Figure 2, which illustrates the entire employed network architecture. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Activation Function** & **Additions** & **Exponentials** & **Divisions** & **Total** \\  & **(1 FLOPs)** & **(4 FLOPs)** & **(3 FLOPs)** & **FLOPs** \\ \hline ReLU (\(\max(0,x)\)) & ✓ & - & - & 1 \\ Sigmoid (\(\sigma(x)=\frac{1}{1+e^{-x}}\)) & ✓ & ✓ & ✓ & 8 \\ Tanh (\(\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\)) & ✓ & ✓ & ✓ & 13 \\ Swish (\(\frac{x}{1+e^{-x}}\)) & ✓ & ✓ & ✓ & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FLOPs per activation functions. The extreme edge devices within the GHs communicate using LoRa modulation at 2.4 GHz, leveraging the capabilities of the latest SX1280 Semtech chip. Compared to other low-power consumption technologies such as ZigBee, SigFox, and IEEE 802.11 ah (Wi-Fi HaLow) [37; 38], LoRa offers several advantages: lower power consumption, greater robustness against interference, and a longer communication range of up to 10 km in line of sight [39; 31]. This is particularly beneficial when GHs are installed in remote and/or rural areas far from the main building, where internet coverage is typically available. Moreover, employing LoRa at a 2.4 GHz frequency enables global compatibility and seamless usability without requiring special configurations or transmission authorization, unlike technologies operating in licensed spectrum. For data processing and sharing, the concept of the cloud continuum is adopted, distributing computation across both the network edge and the centralized public cloud. Communication between these components occurs via the public internet, which is well known to utilize both terrestrial and satellite communications. This approach reduces communication latency for stringent actuation control loops by exploiting edge computing while simultaneously enabling more accurate decision-making and retrospective adjustment of prior decisions, due to the greater computational power offered by centralized cloud computing. The Node-RED low-code/no-code paradigm has been implemented both at the edge and in the cloud to facilitate the integration of new sensing and actuating devices into the GHs and to develop monitoring and control functions. The choice of using the computing server Node-RED is driven by its ability to facilitate data processing through an intuitive and fast block-based programming approach. As the transport protocol, Message Queuing Telemetry Transport (MQTT) has been employed among all entities in the network architecture: extreme edge devices, edge computing, cloud computing, and remote devices. This choice is justified by its simplicity of implementation and low processing overhead on the network [40]. Figure 2: Network architecture. The proposed network architecture also enables human interaction via remote devices, such as dashboards for visualizing monitored data like temperature and humidity trends, water trunk levels and light intensity, and issuing manual commands to be executed in the GHs, digital twins of specific GH services for monitoring their status and prototyping new functionalities, and wearable controllers for transmitting commands to the extreme edge devices [41]. In our application scenario, the developed network architecture consists of four distinct hydroponic greenhouses (\(G=4\)) and two gateways for LoRa coverage (\(L=2\)). Edge computing, incorporating the MQTT broker and Node-RED server, has been implemented on a Raspberry Pi 5 board, while cloud computing utilizes the public cloud Microsoft Azure. More specifically, the extreme edge devices installed in the GHs include [12] high-power LEDs (UV, red, blue, and white), temperature and humidity sensors, ultrasonic sensors, spectrometers, fans, robotic arms, and water pumps.