Precise pear detection and recognition is an essential step toward modernizing orchard management. However, due to the ubiquitous occlusion in orchards and various locations of image acquisition, the pears in the acquired images may be quite small and occluded, causing high false detection and object loss rate. In this paper, a multi-scale collaborative perception network YOLOv5s-FP (Fusion and Perception) was proposed for pear detection, which coupled local and global features. Specifically, a pear dataset with a high proportion of small and occluded pears was proposed, comprising 3680 images acquired with cameras mounted on a ground tripod and a UAV platform. The cross-stage partial (CSP) module was optimized to extract global features through a transformer encoder, which was then fused with local features by an attentional feature fusion mechanism. Subsequently, a modified path aggregation network oriented to collaboration perception of multi-scale features was proposed by incorporating a transformer encoder, the optimized CSP, and new skip connections. The quantitative results of utilizing the YOLOv5s-FP for pear detection were compared with other typical object detection networks of the YOLO series, recording the highest average precision of 96.12% with less detection time and computational cost. In qualitative experiments, the proposed network achieved superior visual performance with stronger robustness to the changes in occlusion and illumination conditions, particularly providing the ability to detect pears with different sizes in highly dense, overlapping environments and non-normal illumination areas. Therefore, the proposed YOLOv5s-FP network was practicable for detecting in-field pears in a real-time and accurate way, which could be an advantageous component of the technology for monitoring pear growth status and implementing automated harvesting in unmanned orchards.

Traditional machine vision is widely used to identify apple quality, but this method finds it difficult to distinguish the apple stem and calyx from defects. To address this, we designed a new method to identify the stem and calyx of apples based on their concave shape. This method applies a fringe projection in a computer vision system of 3D reconstruction, followed by multi-threshold segmentation and a 2D convex hull technique to identify the stem and calyx. A camera and projector were used to reconstruct the 3D surface of the front half of an inspected apple. The height information for each pixel was reconstructed by a fringe projection and mathematical transformation. The 3D-reconstructed result was subjected to a multi-threshold segmentation technique and the segmentation results contained a concave feature in the curved line, representing the concave stem and calyx. The segmentation results were then subjected to a 2D convex hull technique, allowing for the identification of the stem and calyx. This method was evaluated using four groups of apples, and the proposed method is able to identify the stem and calyx with 98.93% accuracy.

Bale density is one of the main performance indicators to measure the quality of baler operation. In this study, a real-time baler bale density monitoring system was designed for the problem of difficult real-time measurement of bale density on round balers. Firstly, a weighing calculation model for the rolling and sliding stage of the bale was established, and the dynamic characteristics during the contact between the bale and the inclined surface were analyzed based on ADAMS dynamics simulation. Then, a real-time monitoring system for the bale density based on the contact pressure of the inclined surface, attitude angle measurement and hydraulic monitoring of the cylinder was constructed, and the accuracy of the weighing model was confirmed. The system was used to observe and analyze the changes in the pitch angle of the carrier table and the oil pressure in the rod chamber of the backpack cylinder during the operation of the round baler. Finally, the monitoring system was calibrated and the dynamic calibration equations were obtained. The results show that the maximum error between the calculated value of the original weighing model and the actual weight was 3.63%, the maximum error of the calculated value of the weighing model corrected by the calibration equations was 3.40% and the measurement accuracy could be satisfied. The results show that the system was highly accurate and met the practical needs of bale weighing in the field.

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm’s performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.

As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.