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.

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.

The film-covered sweet potato transplanting method requires ensuring the transplantation conditions of small planting holes and large lateral displacement. In the soil insertion phase, the transplantation machine requires a mechanism design with multiple timed poses, and the existing design methods are still imperfect. For this reason, this article proposes a multi-target trust region parameter-guided optimization algorithm. This algorithm aims to achieve multi-objective optimization design with more timed pose conditions starting from individual timed pose conditions. First, multi-target problems are decomposed into multiple subproblems, and the parameter arrays are kept with the minimum polymerization value of each subproblem. Then, the approximate function value reduction for each target is calculated using this parameter set, and the step size for the next iteration of each subproblem is determined by comparing this approximate reduction with the actual reduction. After many iteration calculations, the parameter arrays end the calculation when the parameter group is no longer updated. This paper uses the design of a film-covered sweet potato transplanting mechanism as a complex optimized application example. The algorithm is used to obtain the optimization results of the target values of eight groups of institutions. The smallest hole is 2.99 mm, and the horizontal transplanting distance is 108.40 mm. The maximum hole is 17.64 mm, and the horizontal transplanting distance is 124.97 mm. Considering the size of the hole and the horizontal transplanting distance of sweet potato transplanting, the mechanism’s target value of the horizontal transplanting distance at 119.92 mm and the hole size at 0.31 mm were selected to design the sweet potato transplanting machine. The correctness of the results is verified, which reflects the practicability of the algorithm.

Vegetables have the largest planting areas besides grains. Machine vision navigation has been one of the most crucial indicators of mechanization, automation, and intelligence in modern agriculture. Most vegetable transplanters are still manually driven at present. It is also necessary to detect the ridge mounds before the navigation of the transplanter. Since the ridge mounds are often free of crops before transplanting, it is still challenging to find references. Therefore, it is a high demand to extract the navigation lines with crop-free ridges mounds under complex scenes. There was also similar color information and small texture difference in crop-free ridges rows. Traditional image processing cannot fully meet large-scale production. In this study, a ridge row segmentation model was proposed using an improved version of DeepLabV3+. The real-time performance of semantic segmentation was also achieved with the high applicability, accuracy, and detection speed. The traditional DeepLabV3+ network was simplified to replace the Xception backbone network with the MobileNetV2 network. The speed of detection and the real-time performance were obtained after that. The DeepLabV3+ model incorporated the Convolutional Block Attention Module attention mechanism, in order to better treat the ridge boundary information. The important details of the ridge boundary were focused to accurately detect and classify the target objects. Navigational feature points were obtained using the ridge boundary information. In cases where the seedlingless ridges were present, the navigational feature points were deviated from the intended positions. Accordingly, the feature points were adjusted for the guidance of accurate navigation. The quartiles were utilized to filter out any outliers among the navigation feature points. Any data points were identified and removed to deviate significantly from the norm. In addition, the least squares method was used to fit the navigation line using the fitted feature points. A reliable reference of the navigation line was then obtained to compensate for any deviations from the seedlingless ridges. Overall, the simplified DeepLabV3+ network with the MobileNetV2 backbone was incorporated with the CBAM attention mechanism. There were the high detection speed, real-time performance and accurate navigations, even in the challenging scenarios with the ridge boundaries. Two locations were also selected from the images, in order to improve the applicability of the model in the environments of crop-free ridges. The challenge remained on the different soil qualities, lighting conditions and seedlingless ridges in the field test. The dataset consisted of 1 350 images in the training set, and 150 images in the validation set. The images were then expanded using data enhancement. The results indicate that the improved model was achieved with the mean pixel accuracy of 96.27%, the mean intersection and merger ratio of 93.18%, and an average detection frame rate of 84.21 frames per second. The mean intersection over union and mean pixel accuracy of MobileNetV2 model the accuracy is improved by 1.78 and 0.83 percentage points compared to the original model, and the frame rate increase by 29.83 frames per second, while the MobileNetV3 model achieves the mean intersection over union and mean pixel accuracy decreased by 0.83 and 3.28 percentage points respectively, while the frame rate increased 15.47 frames per second. Furthermore, the improved model also demonstrated better average accuracy, average intersection ratio, and frame rate than PSPNet, U-Net, HRNet, Segformer, and DeepLabV3+. The Hough transform and Random Sample Consensus were much less effective in obtaining the navigation lines from different scenes, compared with the maximum angular error of 1.2° and the maximum pixel error of 9 pixels in various ridges environments. These findings can serve as a strong reference for crop-free ridge navigation in agricultural robots, thus promoting the development of intelligent agricultural equipment.

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.

The end-effector is an important part of the broccoli harvesting robot. Aiming at the physical characteristics of a large broccoli head and thick stem, a spherical cutting tool broccoli harvesting end-effector was designed in this study. First, the physical characteristics of broccoli were tested, and physical parameters such as the broccoli head diameter and stem diameter of broccoli were measured. The maximum cutting force of broccoli stems under different cutting angles was tested. Second, according to the physical characteristics and harvesting process of broccoli, the end-effector was designed, and the mathematical model of kinematics and dynamics was established. Based on the results of dynamic analysis, the end-effector rod was optimized, and the unilateral width of the slider was 40 mm, the length of the connecting rod was 120 mm, and the length of the crank was 42 mm. The mechanism needed an external driving force of 140.54 N to cut the broccoli stem. Therefore, a 32 mm cylinder with a load rate of 50% was selected as the power source. Finally, the feasibility of the broccoli harvesting end-effector was verified by the harvesting test. Experiments showed that the overall harvesting success rate of the end-effector is 93.3%, and the smoothness rate of the stem section is 83.3%. The harvesting performance of the broccoli end-effector was verified. This lays a foundation for agricultural robots to harvest broccoli.