This paper introduces a UAV-based multispectral model, YOLO-SPAD, for rapid, non-destructive estimation of relative chlorophyll content of tea leaves. This is crucial for predicting growth conditions, implementing precise irrigation and fertilization, and increasing tea yields. The model leverages the spectral separation of the tea tree canopy and the YOLOv8 neural network architecture for image segmentation. Multispectral imagery of the standardized tea plantation was captured by a UAV equipped with a five-channel camera, while corresponding SPAD values were measured using a SPAD-502Plus device. Based on the YOLOv8 model, it is proposed to incorporate a Spectral Fusion module to better adapt to multispectral characteristics. Four deep learning models for image segmentation were further compared. The improved YOLOv8 model achieved a segmentation mIoU of 91.61% for the tea tree canopy, outperforming the YOLOv5 (90.65%) and DeepLabv3+ (86.14%), which were improved using the same method. Five different band combinations, 43 vegetation indexes, and 40 texture features were analyzed to construct mapping transformation pairs for reflectance in the segmented area. This was applied to the SPAD prediction head to implement YOLO-SPAD. The YOLO-SPAD model was used to predict tea canopy SPAD using UAV multispectral imagery with a coefficient of determination (R²) of 0.85 and a low error (RMSE=2.14, MAE=1.79), providing accurate and stable predictions. This model supports dynamic monitoring of tea tree growth via UAV remote sensing, aiding crop nutrition improvement and precision agriculture implementation.

Virtual machines (VMs) have been increasingly deployed in real-time systems to enhance heterogeneous resource sharing. Despite being isolated for security and prevention of failure propagation, VMs need to communicate with each other to complete certain tasks jointly. A real-time inter-VM communication framework has been proposed based on VirtIO, which is in essence a mechanism of message passing with high latency and low scalability on data amount. In contrary, IVSHMEM implements inter-VM communication with shared memory, which is generally fast and large in throughput. Unfortunately, IVSHMEM does not resolve resource contention between flows and hence cannot be applied in real-time scenarios. In this work, we propose a real-time inter-VM communication framework RTISM built upon shared memory and IVSHMEM. RTISM provides priority-based scheduling and supports mixed-criticality flows. Worst-Case Response Time (WCRT) analysis is reported to bound the end-to-end communication delay and a Limited Priority Assignment (LPA) algorithm is developed to enhance schedulability. Experimental evaluation shows that (i) RTISM has about 4 times higher throughput than the VirtIO-based inter-VM communication framework; (ii) LPA improves schedulability by over 25% compared to the state-of-the-art; (iii) WCRT produces a tight bound.