Identifying the maturity of asparagus is a crucial step for machine-assisted harvesting of asparagus in complex environments. This study proposes an innovative method to evaluate the height and diameter of asparagus stems, combining an enhanced YOLOv5 detection algorithm with point cloud data. In this method, first, the YOLOv5 model was improved, enabling efficient recognition and detection of asparagus in complex environments. Subsequently, a RealSense L515 radar camera was deployed to capture both the original RGB images and the point cloud information. The improved YOLOv5 algorithm was then employed to detect asparagus instances within the RGB images, with the pixel positions of the detection frames mapped onto the point cloud dataset to extract comprehensive 3D point cloud details of the asparagus. Finally, noise was reduced through statistical filtering and Euclidean clustering, and asparagus height was determined using the oriented bounding box methodology. Slices, each with a thickness of 10 mm, were extracted at designated measurement points, and the asparagus diameter was calculated by assessing the disparity between the maximum and minimum coordinates perpendicular to the growth direction of the asparagus. Experimental results showed that the mean average precision, precision, and recall of the improved YOLOv5 model increased by 4.85%, 5.09%, and 3.4%, reaching 98.21%, 97.11%, and 95.33%, respectively, which are higher than those of the YOLOv5 prototype network. Therefore, the proposed method could effectively detect asparagus. The algorithm exhibited a mean absolute error of 1.08 cm, a mean absolute percentage error of 4.06%, and a root mean square error of 1.60 cm in its estimation of asparagus height. For asparagus diameter estimation, the algorithm achieved a mean absolute error of 0.86 mm, a mean absolute percentage error of 7.98%, and a root mean square error of 1.23 mm. These results confirm that the proposed method can estimate the height and diameter of asparagus stems accurately, thereby providing invaluable technical support for machine harvesting of asparagus.

Lotus seedpod is one of the most important components of the lotus flowers. It is often required for the accurate and efficient detection of lotus seedpod maturity under natural environments in intelligent harvesting and precision agriculture. However, there are variable lighting conditions, as well as frequent occlusion by stems and leaves, due mainly to the small size of the lotus seedpods under complex backgrounds. Conventional object detection models can be limited in maintaining high accuracy in such environments, especially under strong light, weak light, or severe occlusion. In this study, the LotusM-YOLO model was proposed to enhance the YOLOv10n architecture after a series of targeted improvements. The great contributions included three key enhancements. Firstly, the dynamic convolution (DynamicConv) module was integrated into the backbone of the YOLOv10n model to enhance its adaptability under varying lighting conditions. The multiple convolutional kernels were dynamically combined to effectively extract the robust features from the images under the strong or low light environments. The irrelevant background noise was suppressed to preserve the essential features of the lotus seedpods, thereby significantly enhancing the detection accuracy and stability in natural paddy field scenes with complex illumination. Secondly, the Multi-scale efficient attention module (MultiSEAM) was improved to detect the small and partially occluded lotus seedpods, where the contextual information was captured over multiple feature scales. At the same time, some interference was further suppressed from the complex backgrounds in order to enhance the robustness under visually cluttered environments. Finally, the convolutional block attention module (CBAM) was sequentially applied as the channel and spatial attention to refine the feature representation. The detection precision was effectively enhanced for the lotus seedpods. The rate of the missed detections was significantly reduced using the attention mechanism. Together, these attention modules synergistically strengthened the sensitivity to the occluded and small targets under natural environments, in order to maintain the high detection accuracy. The performance of the LotusM-YOLO model was evaluated after optimization. A high-quality dataset contained 2 411 manually annotated images of lotus seedpods under natural conditions. The dataset was randomly divided into the training, validation, and test sets at a 7:2:1 ratio. The experimental results show that the LotusM-YOLO achieved a precision of 84.3%, a recall of 81.7%, and a mean average precision at IoU 0.5 (mAP_0.5) of 86.7%, indicating an increase of 2.7 percentage points, 2.5 percentage points, and 3.9 percentage points, respectively, over the YOLOv10n baseline. Subsequently, the comparative experiments were conducted using multiple detection models, including Faster R-CNN, YOLOv5n, YOLOv8n, YOLOv9, and YOLOv10n. The results demonstrated that the LotusM-YOLO model achieved higher detection precision and recall under strong light, low light, and partial occlusion conditions, in order to significantly reduce the missed detections. The LotusM-YOLO model also exhibited stronger robustness in the lotus seedpod detection tasks under natural environmental conditions. Additionally, the heatmaps were generated using Gradient-weighted Class Activation Mapping (Grad-CAM). The improved model was more focused on the actual target areas. The attention was reduced to the background clutter, especially compared with the YOLOv10n model. Beyond technical performance, the LotusM-YOLO model can offer strong potential for real-world applications. The detection can be integrated with the depth data from RGB-D or LiDAR sensors. The accurate 3D localization of seedpods can guide the robotic arms in the picking tasks of the lotus seedpod. Consequently, the LotusM-YOLO model can provide a theoretical basis to monitor the growth status of the lotus seedpods for the intelligent harvesting equipment under a natural environment.

To meet the requirements of low damage and high seedling-standing of vegetable plug seedlings during mechanical transplanting, a new seedling planting mechanism combining non-circular gear system and multi-link was developed, which consisted of a five-bar seedling pushing mechanism with the planetary gear train of non-circular gears and a cam linkage seedling supporting mechanism. Based on the agronomic requirements for transplanting vegetable plug seedlings, the design requirements and the poses of four key points of the seedling pushing mechanism were determined. The mechanism solution region synthesis method was applied to conduct precise four poses synthesis of the five-bar seedling pushing mechanism. Based on four key pose points and five additional positions of the seedling pushing trajectory, the angular variation curve of the connecting rod relative to the planetary carrier was derived through cubic B-spline curve fitting. Subsequently, the kinematics analysis of the seedling pushing mechanism was performed to complete continuous motion discrimination and determine the parameters of the five-bar seedling pushing mechanism. The kinematics model of the planetary gear train with non-circular gears was established, and the pitch curve design for each gear was undertaken to fulfill the design of the seedling pushing mechanism. According to the motion law of the seedling pushing mechanism and the seedling planting requirements, the kinematics model of the seedling supporting mechanism was established and its parameters were solved. The virtual simulation analysis and prototype experiment of the mechanism confirm that the simulation and test motion trajectory and key poses are basically consistent with the theoretical design results. Moreover, when the rotation speeds of the seedling planting mechanism are 60-80 r/min, the success rates of broccoli and pepper seedling planting all reach over 93%, indicating the correctness of the seedling planting mechanism design and its application feasibility and universality.

Aiming at the lack of suitable machines for sweet potato seedling transplanting in China, and according to the agronomic requirements for the horizontal insertion method of sweet potato seedling, a new sweet potato seedling transplanting mechanism of planetary gear train was proposed based on the non-uniform transmission of deformed elliptical gear. The working principle of the transplanting mechanism was analyzed, and the kinematics modeling and analysis of the mechanism were carried out. The study established the numerical objectives of the transplanting mechanism and applied the theory of membership function to establish a mathematical model for the parameter-guided optimization design of the transplanting mechanism. The parameter-guided optimization design software was developed to obtain a set of optimal mechanism parameters that satisfied the motion trajectory of sweet potato transplanting and the posture of the transplanting arm. Based on the optimized parameters, the structure of the transplanting mechanism was designed, and a virtual prototype of the mechanism was created, whereby a virtual motion simulation of the transplanting mechanism was conducted to verify the correctness of the kinematics model and design of the mechanism. The high-speed photographic kinematics test of the mechanism prototype and sweet potato seedling transplanting tests were conducted to test the mechanism’s kinematic characteristics and transplanting performance. The test results show that the test trajectory of the mechanism and test posture of the transplanting arm are almost consistent with the theoretical and simulation trajectory, meeting the agronomic requirements of the horizontal insertion method of sweet potato seedling; And when the rotary speed of the mechanism are 20 r/min and 30 r/min, the average success ratios of sweet potato seedlings transplanting are 90% and 82%, respectively, which prove the application feasibility of the mechanism in the practical machines.