Weather and soil water dictate farm operations such as irrigation scheduling. Low-cost and open-source agricultural monitoring stations are an emerging alternative to commercially available monitoring stations because they are often built from components using open-source, do-it-yourself (DIY) platforms and technologies. For irrigation management in an experimental vineyard located in Quiroga (Lugo, Spain), we faced the challenge of installing a low-cost environmental and soil parameter monitoring station composed of several nodes measuring air temperature and relative humidity, soil temperature, soil matric potential, and soil water content. Commercial solutions were either too expensive or did not meet our needs. This challenge led us to design the low-cost sensor system that fulfilled our requirements. This node is based on the ESP32 chip, and communication between the nodes and the gateway is carried out by LoRa technology. The gateway is also based on the ESP32 chip. The gateway uploads the data to an FTP server using a Wi-Fi connection with a 4G router while simultaneously storing the data on a memory card. The programming of the code for the nodes and the gateway is performed using the Arduino IDE. The equipment developed is proven to be effective and for managing vineyard irrigation based on the built-in sensors, with replicable results. It is, however, essential to calibrate the capacitive sensors for measuring soil water content in each soil type in order to enhance their ability to produce reliable results. In addition, the limits marking the beginning and end of irrigation tasks must be adjusted to local conditions and according to the producer’s specific vineyard objectives.

The impact of different variability sources on the transistor performance increases as devices are scaled-down, being the metal grain granularity (MGG) and the line edge roughness (LER) some of the major contributors to this increase. Variability studies require the simulation of large samples of different device configurations to have statistical significance, increasing the computational cost. A novel Pelgrom-based predictive (PBP) model that estimates the impact of MGG and LER through the study of the threshold voltage standard deviation $\left(\sigma V_{T h}\right)$ , is proposed. This technique is computationally efficient since once the threshold voltage mismatch is calculated, $\sigma V_{T h}$ can be predicted for different gate lengths $\left(L_{g}\right)$ , cross-sections, and intrinsic variability parameters, without further simulations. The validity of the PBP model is demonstrated for three state-of-the-art architectures (FinFETs, nanowire FETs, and nanosheet FETs) with different $L_{g}$ , cross-sections, and drain biases $\left(V_{D}\right)$ . The relative errors between the predicted and simulated data are lower than 10%, in the 92% of the cases.