Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5.

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.

Foxtail Millet Ear Detection Method Based on Attention Mechanism and Improved YOLOv5.

In the foxtail millet field, due to the dense distribution of the foxtail millet ears, morphological differences among foxtail millet ears, severe shading of stems and leaves, and complex background, it is difficult to identify the foxtail millet ears. To solve these practical problems, this study proposes a lightweight foxtail millet ear detection method based on improved YOLOv5. The improved model proposes to use the GhostNet module to optimize the model structure of the original YOLOv5, which can reduce the model parameters and the amount of calculation. This study adopts an approach that incorporates the Coordinate Attention (CA) mechanism into the model structure and adjusts the loss function to the Efficient Intersection over Union (EIOU) loss function. Experimental results show that these methods can effectively improve the detection effect of occlusion and small-sized foxtail millet ears. The recall, precision, F1 score, and mean Average Precision (mAP) of the improved model were 97.70%, 93.80%, 95.81%, and 96.60%, respectively, the average detection time per image was 0.0181 s, and the model size was 8.12 MB. Comparing the improved model in this study with three lightweight object detection algorithms: YOLOv3_tiny, YOLOv5-Mobilenetv3small, and YOLOv5-Shufflenetv2, the improved model in this study shows better detection performance. It provides technical support to achieve rapid and accurate identification of multiple foxtail millet ear targets in complex environments in the field, which is important for improving foxtail millet ear yield and thus achieving intelligent detection of foxtail millet.

Maresin 1 alleviates dextran sulfate sodium-induced ulcerative colitis by regulating NRF2 and TLR4/NF-kB signaling pathway.

OBJECTIVE: Ulcerative colitis (UC) is one of the most common gastrointestinal diseases, characterized as a chronic, relapsing inflammation that causes damage to the colonic mucosa. Maresin 1 (MaR1), a specialized proresolving mediator, has powerful anti-inflammatory activity that prevents the occurrence of various inflammatory diseases. The aim of this study was to explore the role and potential mechanism of MaR1 in DSS-induced ulcerative colitis. METHODS: In the present study, we established dextran sulfate sodium (DSS)-induced ulcerative colitis rat model in vivo. Rats with colitis received tail vein injection of MaR1, with or without intraperitoneal injection of ML385. The changes of body weight, colon length, disease activity index (DAI), colonic histopathology, inflammatory cytokines, the activity of myeloperoxidase (MPO) and reactive oxygen species (ROS), and infiltration of macrophages expressing F4/80 were analyzed for the evaluation of colitis severity. In addition, protein expressions were detected using western blot. RESULTS: MaR1 significantly reduced inflammatory cytokines production, and restored body weight, DAI and colonic histopathology. Besides, MaR1 improved the expression of tight junction (TJ) proteins and reduced the infiltration of neutrophil and macrophages, as well as a decreased activity of MPO and ROS. Meanwhile, MaR1 activated Nrf2 signaling and decreased toll-like receptor 4(TLR4)/nuclear factor-κB(NF-κB) activation. Furthermore, ML385, an inhibitor of Nrf2, significantly reversed the protective effect of MaR1. CONCLUSION: MaR1 play a protective role in DSS-induced colitis by activating Nrf2 signaling and inactivating Nrf2-mediated TLR4/NF-κB signaling pathway, which mediate proinflammatory mediators and intestinal TJ proteins in rats, providing novel insights into the therapeutic strategy of colitis.