The honey bee (Apis mellifera) is of great significance to both the ecological environment and human society, providing bee products and making a significant contribution to the pollination of crops

Convolutional neural networks(CNNs) have been widely applied in various applications. However, the computation-intensive convolutional layers and memory-intensive fully connected layers have brought many challenges to the implementation of CNN on embedded platforms. To overcome this problem, this work proposes a power-efficient accelerator for CNNs, and different methods are applied to optimize the convolutional layers and fully connected layers. For the convolutional layer, the accelerator first rearranges the input features into matrix on-the-fly when storing them to the on-chip buffers. Thus the computation of convolutional layer can be completed through matrix multiplication. For the fully connected layer, the batch-based method is used to reduce the required memory bandwidth, which also can be completed through matrix multiplication. Then a two-layer pipelined computation method for matrix multiplication is proposed to increase the throughput. As a case study, we implement a widely used CNN model, LeNet-5, on an embedded device. It can achieve a peak performance of 34.48 GOP/s and the power efficiency with the value of 19.45 GOP/s/W under 100MHz clock frequency which outperforms previous approaches.

Both hive displacement and entrance relocation can challenge honeybee homing navigation, yet the temporal dynamics of colony-level homing behavior following hive entrance displacement remain poorly quantified. To address this, we established a non-invasive automated pipeline integrating YOLO11m-based detection, OC-SORT tracking, and the Homing Rate (HR) to monitor nine honeybee (Apis mellifera) colonies under semi-natural apiary conditions. HR links trajectory endpoints with the experimentally defined valid entrance state and provides a colony-level measure of entrance-targeting accuracy. Horizontal entrance displacement caused a substantial reduction in HR in the treated colonies, whereas the Control Group showed only a small concurrent change. During the subsequent four-day observation period, all six treated colonies displayed a similar dynamic pattern characterized by an initial rapid increase in HR, followed by a slower increase. The asymptotic exponential model provided a better descriptive representation of these temporal dynamics than a linear model. The 14-day observation of Colony A1 further revealed an early increase, a transient decline, and subsequent recovery toward a relatively stable level, indicating that the post-displacement trajectory was not strictly monotonic. Overall, this study provides an automated quantitative pipeline for continuously characterizing colony-level entrance-targeting behavior and its temporal dynamics following hive entrance displacement.