Safflower has often drawn much attention in the field of intelligent harvesting, due to their economic value. The safflower harvesting has also posed the higher requirements for object detection, due to the large-scale variations and complex occlusion in natural environments. Furthermore, missed or false detection has often occurred in traditional object detection, thus seriously affecting picking efficiency and accuracy. In this study, a YOLO-SSAR object detection was proposed to optimize the original YOLOv5s model using multi-scale feature extraction. The effectiveness and rationality of the improved algorithm were verified through ablation experiments, model comparison, and detection effect analysis. Firstly, the ShuffleNet v2 lightweight structure was used to replace the backbone feature extraction network of the backbone layer, in order to reduce the number of model parameters and calculations. The efficient channel mixing and depthwise separable convolution were utilized to improve the efficiency of input feature extraction. Secondly, a Scale-Aware RFE module was added to the Neck layer using dilated convolution and shared weights, in order to extract the multi-scale features. The weights of the main branch were shared with the rest branches, thus lowering the number of model parameters. The risk of overfitting was also reduced to fuse the residual connections, thereby allowing the objects of different scales to be uniformly transformed with the same representation. Finally, the repulsion loss function was introduced into the head layer to replace the original loss function, in order to avoid the intra-class and inter-class occlusion in the object detection. There was a reduction in the missed or false detection caused by improper selection of the non-maximum suppression (NMS) threshold. The detection rate of the target was improved with the overlap occlusion in dense scenes. The experimental results showed that the precision, recall, and mean average precision of the YOLO-SSAR algorithm on the test set were 90.1%, 88.5%, and 93.4%, respectively. Compared with the original YOLOv5s model, the YOLO-SSAR algorithm was improved by 5.9, 9.2, and 7.7 percentage points, respectively. The inference speed reached 115 frames per second, and the model size was 9.7 MB, indicating high efficiency and lightweight in practical applications. Compared with the mainstream algorithms YOLOv4, YOLOv7, YOLOV8s, Faster R-CNN, and SSD, the detection accuracy of the YOLO-SSAR algorithm was in a leading position. The improved model was 5.5 times and 3.6 times that of the two-stage and multi-scale object detection of Faster R-CNN and SSD respectively. Meanwhile, the model size was only 4% of Faster R-CNN and 10% of SSD. The minimum quantity of parameters shared the great prospects in the mobile devices with the limited computing resources. The precision was 6.8, 7.2, 6.3, 16.2, and 10.8 percentage points higher, the recall was 9.4, 10.3, 9.5, 17.3, and 59.4 percentage points higher, and the mean average precision was 8.8, 8.2, 8.1, 14.9 and 19.4 percentage points higher than the mainstream algorithm, respectively. The YOLO-SSAR algorithm improved the detection performance with less computational complexity. The findings can provide the algorithm references for the intelligent harvesting of safflower.

Visual navigation of robots has been widely used to extract information from their surroundings to determine subsequent activity in agricultural fields. However, the navigation accuracy has been limited to the very complex scene in the field, particularly under light variation and plant growth. It is also difficult to extract the crop path using sparse cotton seedlings, missing seedlings, and weeds in the cotton seedling stage scenario. In this study, a visual navigation method was established using improved RANSAC (random sample consensus). A series of experiments was also carried out on navigation path tracking in the cotton field. Firstly, the images were captured at multiple growth stages during cotton seedlings using agricultural robot with a camera. Then the crop rows and background were distinguished to fully separate by adaptive threshold segmentation. The binary images were also denoised using morphological filtering. According to the region of interest, the outlier points beyond the crop row were removed from the image by the improved RANSAC algorithm. The detection and clustering of feature points were carried out to ensure the accuracy of the final extracted center line of the crop row. Finally, the navigation path was obtained after the least square fitting. The experimental results show that the better fitting path was obtained after the removal of the outliers using improved RANSAC, which was in line with the actual position for the center line of the crop row. Specifically, the line recognition rate was 96.5% using the traditional RANSAC algorithm, the average error angle was 1.41°, and the average time of image processing was 0.087 s. By contrast, the line recognition rate increased to 98.4% after removing outliers by the improved RANSAC algorithm, and the average error angle was only 0.53°. The performance of center line extraction was significantly improved using a modified RANSAC algorithm, compared with the original. In addition, a comparison was also made between the improved and traditional Hough transform. The effectiveness of the improved model was then verified to extract the navigation path. The spraying robot with visual navigation was self-developed to better validate the practical application of this improved model in a complex environment. The path-tracking experiments were then conducted autonomously in the field of cotton seedling. Three initial states and three moving speeds were selected, including 0.4, 0.5, and 0.6 m/s. Image processing was realized using OpenCV with robot operating system (ROS). Furthermore, an open-source library was also configured during image processing using machine vision. A path-tracking algorithm was utilized to improve the tracking accuracy using adaptive sliding-mode control. Among them, the maximum lateral deviations of the robot were 1.53, 2.29, and 2.59 cm, respectively, when the speed values were 0.4, 0.5, and 0.6 m/s, respectively. There were no rolling failures, fully meeting the precision requirements of the application robot for the line operation in the cotton field under the planting mode of "1 film, 3 ridges, and 6 rows". Visual navigation can also provide the theoretical support and technical basis for the autonomous navigation and mobile operation of agricultural robots on the farm.

From the perspective of numerical simulation, taking the discharge state as the main differentiation point, focusing on the discharge channel, energy distribution ratio, discharge crater, and material removal process involved in the discharge process, this paper summarizes the current progress of the research on the electric discharge machining(EDM) method, the mechanism of electric spark machining and electric arc machining, and puts forward the trend of the future numerical simulation research on the EDM mechanism, focusing on the equivalent heat source, multi-physics field and molecular dynamics composite simulation, and electric arc machining mechanism, to reveal more accurately the mechanism behind EDM and to solve the difficult problem that it is difficult to achieve both efficiency and quality of electric arc machining.

This paper takes titanium alloy Ti-6Al-4V(TC4) as the research object, establishes the mathematical model of process characteristic parameters (milling depth, milling width, electrode diameter) and milling area, and reveals the mathematical relationship between each characteristic parameter and milling area. Arc milling experiments were carried out to study the effect of milling area on processing current, processing quality and processing efficiency. The results show that: changes in the characteristic parameters affect the material etching rate, resulting in changes in the arc discharge energy, which in turn affects the thickness of the heat-affected layer and the recast layer; the average peak current, surface roughness value and the thickness of the heat-affected layer are positively correlated with the milling area, while the maximum permissible feed rate is negatively correlated with the milling area; under the condition of DC voltage of 20 V, the milling depth is 3 mm, the milling width is 2 mm, and the spindle speed is 1 000 r/min, the surface quality is the best, the surface roughness value is 29.31 μm, the thickness of heat-affected layer is 88.24μm, and the maximum material removal rate is 4 996.2 mm³/min. It is proved that the analysis of the milling area is more helpful for the subsequent arc milling machining process regulation and optimization.