Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue. This study implements an Artificial Intelligence of Things (AIoT) system based on fog computing on a smart farm. Three experiments are conducted to evaluate the performance of the AIoT system. First, network traffic volumes between systems employing and not employing fog computing are compared. Second, the performance of the communication protocols—hypertext transport protocol (HTTP), message queuing telemetry transport protocol (MQTT), and constrained application protocol (CoAP)—commonly used in IoT applications is assessed. Finally, a convolutional neural network-based algorithm is introduced to determine the maturity level of coffee tree images. Experimental data are collected over ten days from a coffee tree farm in the Republic of Korea. Notably, the fog computing system demonstrates a 26% reduction in the cumulative data volume compared with a non-fog system. MQTT exhibits stable results in terms of the data volume and loss rate. Additionally, the maturity level determination algorithm performed on coffee fruits provides reliable results.

There has been a surge in research focused on the analysis of vital signs using remote photoplethysmography (rPPG) sensors, as opposed to traditional photoplethysmography (PPG) methods. Unlike PPG, rPPG imposes no spatial constraints and employs a straightforward measurement technique, making it increasingly prevalent. Its integration into image processing, harnesses the remarkable advances in artificial-intelligence technology, achieving accuracy that is comparable to that of traditional PPG sensors. In prior studies, obtaining vital signs often necessitated an unnecessary and procedural fixation of facial positions within frames to enhance predictive accuracy. Despite such fixation, achieving notably high accuracy remained elusive. Here, we introduce a simple yet robust approach utilizing videos captured by an rPPG sensor, ensuring both high accuracy and resilience to noise. We propose a convolutional neural network model meticulously designed to resist interference from noise data that may arise in the initial stages, coupled with effective preprocessing techniques to attain superior predictive accuracy. Data extracted by a facial extractor undergoes preprocessing via normalization. Leveraging the Temporal Shift Module (TSM), this normalization efficiently captures temporal relationships without incurring additional computational overhead. Mitigating noise signal interference from non-facial data through the use of multiple attention masks and augmenting prediction accuracy via skip connections. Moreover, we compile a specially tailored dataset for pulse rate and breath rate data, catering specifically to the East Asian population. The proposed process demonstrates outstanding performance in predicting both pulse rate and breath rate.