In the factory nursery, qualified seedlings can be used to replant unqualified seedlings or missing seedlings in the seedling tray through automatic transplanters. Due to the random positions of unqualified and missing seedlings, the end effector of the automatic replanting machine spends substantial time shuttling between the supply tray and the target tray to complete the replanting task. Therefore, we proposed a fast path planning method based on improved particle swarm optimization and compared it with the fixed sequence method and genetic algorithm in experiments with different replanting numbers in different tray types. The experiment shows that the improved particle swarm optimization algorithm and genetic algorithm can shorten the length of the replantation path by about 20% compared with the fixed sequence method, and the running time of the improved particle swarm optimization algorithm is 57.63% less than the genetic algorithm on average. The replanting path optimization method based on improved particle swarm optimization designed in this research can significantly optimize the length and time of the replanting path of the seedling tray, improve the efficiency of the replanting operation, and meet the real-time requirements.

Optical coherence tomography (OCT) image processing can provide information about the uterine cavity structure, such as endometrial surface roughness, which is important for the diagnosis of uterine cavity lesions. The accurate segmentation of uterine cavity OCT images is a key step of OCT image processing. We proposed an EA-UNet-based image segmentation model that uses a U-Net network structure with a multi-scale attention mechanism to improve the segmentation accuracy of uterine cavity OCT images. The E(ECA-C) module introduces a convolutional layer combined with the ECA attention mechanism instead of max pool, reduces the loss of feature information, enables the model to focus on features in the region to be segmented, and suppresses irrelevant features to enhance the network’s feature-extraction capability and learning potential. We also introduce the A (Attention Gates) module to improve the model’s segmentation accuracy by using global contextual information. Our experimental results show that the proposed EA-UNet can enhance the model’s feature-extraction ability; furthermore, its MIoU, Sensitivity, and Specificity indexes are 0.9379, 0.9457, and 0.9908, respectively, indicating that the model can effectively improve uterine cavity OCT image segmentation and has better segmentation performance.

During the replanting operation of a seedling tray, the end-effector needs to repeatedly grab the qualified plug seedlings in the supply tray and release them to the target tray for replanting, and in the process of grasping, the end-effector may cause some mechanical damage to the plug seedlings, thus affecting their quality. Therefore, in order to be able to adjust the position of the hand claw grasping point according to the morphological characteristics of the plug seedlings and select the optimal grasping point, this paper proposes research on the optimal grasping angle algorithm for plug seedlings based on machine vision. Firstly, a rotatable three-jaw end-effector is designed, which uses a three-jaw structure for grasping the burrowing seedlings. The three claws are driven with a telescopic cylinder to carry out clamping and relaxing actions. The rotation of the three claws is controlled with the stepper motor to adjust the optimal grasping position. Secondly, based on the pre-processing of an image of the hole tray seedling, the extraction of feature points in the region of interest, and the calculation of localization, the angle between the angular bisector of the cotyledon leaf blade of the hole tray seedling and the horizontal positive direction is solved. In this paper, two methods are designed to calculate the coordinates of feature points: one is the geometric method and the other is the center-of-mass method. Finally, the optimal grasping angle is calculated by analyzing the angle between the angular bisector of the cotyledon leaf blade and the horizontal positive direction of the cavity seedlings. According to the test, the average calculation error of the proposed algorithm is 3.12 degrees, and the average calculation time is 0.512 sec/sheet, which meet the requirements of the replanting operation.

In the process of plug seedling transplantation, the cracking and dropping of seedling substrate or the damage of seedling stems and leaves will affect the survival rate of seedlings after transplantation. Currently, most research focuses on the reduction of substrate loss, while ignoring damage to the hole tray seedling itself. Targeting the problem of high damage rate during transplantation of plug seedlings, we have proposed an adaptive grasp method based on machine vision and deep learning, and designed a lightweight real-time grasp detection network (LRGN). The lightweight network Mobilenet is used as the feature extraction network to reduce the number of parameters of the network. Meanwhile, a dilated refinement module (DRM) is designed to increase the receptive field effectively and capture more contextual information. Further, a pixel-attention-guided fusion module (PAG) and a depth-guided fusion module (DGFM) are proposed to effectively fuse deep and shallow features to extract multi-scale information. Lastly, a mixed attention module (MAM) is proposed to enhance the network’s attention to important grasp features. The experimental results show that the proposed network can reach 98.96% and 98.30% accuracy of grasp detection for the image splitting and object splitting subsets of the Cornell dataset, respectively. The accuracy of grasp detection for the plug seedling grasp dataset is up to 98.83%, and the speed of image detection is up to 113 images/sec, with the number of parameters only 12.67 M. Compared with the comparison network, the proposed network not only has a smaller computational volume and number of parameters, but also significantly improves the accuracy and speed of grasp detection, and the generated grasp results can effectively avoid seedlings, reduce the damage rate in the grasp phase of the plug seedlings, and realize a low-damage grasp, which provides the theoretical basis and method for low-damage transplantation mechanical equipment.

With the increase in the amount of 3D point cloud data and the wide application of point cloud registration in various fields, the question of whether it is possible to quickly extract the key points of registration and perform accurate coarse registration has become a question to be urgently answered. In this paper, we proposed a novel semantic segmentation algorithm that enables the extracted feature point cloud to have a clustering effect for fast registration. First of all, an adaptive technique was proposed to determine the domain radius of a local point. Secondly, the feature intensity of the point is scored through the regional fluctuation coefficient and stationary coefficient calculated by the normal vector, and the high feature region to be registered is preliminarily determined. In the end, FPFH is used to describe the geometric features of the extracted semantic feature point cloud, so as to realize the coarse registration from the local point cloud to the overall point cloud. The results show that the point cloud can be roughly segmented based on the uniqueness of semantic features. The use of a semantic feature point cloud can make the point cloud have a very fast response speed based on the accuracy of coarse registration, almost equal to that of using the original point cloud, which is conducive to the rapid determination of the initial attitude.

As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production.