The blood flow velocity in the nailfold capillary is an important indicator of the status of microcirculation. The conventional manual processing method is both laborious and prone to human artifacts. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. Inspired by the Two-Stream Convolutional Networks, this study proposes an optical flow-assisted two-stream network to segment nailfold blood vessels. Firstly, we use U-Net as the spatial flow network and the dense optical flow as the temporal stream. The results show that the optical flow information can effectively improve the integrity of the segmentation of blood vessels. The overall accuracy is 94.01 %, the Dice score is 0.8099, the IoU score is 0.6806, and the VOE score is 0.3194. Secondly, The flow velocity of the segmented blood vessel is determined by constructing the spatial-temporal (ST) image. The blood flow velocity evaluated is consistent with the typical blood flow speed reported. This study proposes a novel two-stream network for blood vessel segmentation of nailfold capillary images. Combined with ST image and line detection method, it provides an effective workflow for measuring the blood flow velocity of nailfold capillaries.

Pine wilt disease is a destructive forest disease caused by the parasitism of pine wood nematodes inside pine trees. Once infected, pine trees quickly wither and die due to the inability to drain water, hence it is also known as pine tree withering disease. It is mainly spread by pine sawyer beetles, with the characteristics of fast dissemination, rapid onset, and high mortality rate, making it a significant global plant disease. Failure to effectively control the pine wilt disease in a timely manner will result in the massive death of pine trees in a short period of time in forest areas. With the continuous development and popularization of artificial intelligence and UAV (Unmanned Aerial Vehicle) technology, combining various methods can timely detect diseased trees, and performing timely treatment and protection can greatly save the time and personnel costs of biological pest control. While it is also conducive to promoting the work of multiple related disciplines, such as bioengineering and greening engineering. In this paper, we first conducted a large number of aerial surveys on the forest area through UAV, collected relevant data, and preprocessed the obtained data, including data augmentation, cleaning, filtering, deduplication, formatting, etc., to ensure data quality and accuracy. Then, we labeled the data, and made certain improvements to the original YOLOv5. We added a new RRAM (Recurrent Residual Attention Module) to the original network model, which enables the network to timely focus on important information in redundant data, thus improving the network’s performance. Compared with the original YOLOv5, our network has a stronger performance.

Aiming at the problems of low recognition accuracy of detector algorithm caused by occlusion interference of grape disease spot in natural complex background, non-significant visual features of grape spot, small target of disease spot and uneven number of sample categories, this study proposed an improved model method based on YOLOv5s. The new WiseIoU is used as the loss function to calculate the positioning loss, so as to improve the accuracy of the algorithm to predict the positioning and speed up the convergence of the model. CA attention mechanism was added to the basic network to enhance grape lesion feature extraction. A small target detection layer was added to the head network of the model to improve the recognition and detection ability of grape small target lesions. IDADP-grape disease recognition image dataset is taken as the main dataset of this study, and combined with self-collected grape disease images, the experimental data set is composed and the training test is carried out. The experimental results show that the improved algorithm mAP 0.5 reaches 84.40%, which is 1.30% higher than the baseline model. Compared with other detector algorithms, mAP0.5 and recognition accuracy have improved to different degrees. This method has strong antiinterference robustness and can be used for rapid and accurate identification of grape diseases in complex field environment.