Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]