This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7) to address the low accuracy of apple fruit target recognition caused by high fruit density, occlusion, and overlapping issues. Firstly, we proposed a preprocessing algorithm for the split image with overlapping to improve the robotic intelligent picking recognition accuracy. Then, we divided the training, validation, and test sets. Secondly, the MobileOne module was introduced into the backbone network of YOLOv7 to achieve parametric fusion and reduce network computation. Afterward, we improved the SPPCSPS module and changed the serial channel to the parallel channel to enhance the speed of image feature fusion. We added an auxiliary detection head to the head structure. Finally, we conducted fruit target recognition based on model validation and tests. The results showed that the accuracy of the improved YOLOv7 algorithm increased by 6.9%. The recall rate increased by 10%, the mAP1 algorithm increased by 5%, and the mAP2 algorithm increased by 3.8%. The accuracy of the improved YOLOv7 algorithm was 3.5%, 14%, 9.1%, and 6.5% higher than that of other control YOLO algorithms, verifying that the improved YOLOv7 algorithm could significantly improve the fruit target recognition in high-density fruits.
As a vector of pine wood nematode disease, Monochamus alternatus is wood-boring, cryptic, and extremely difficult to control—features that have made them a disastrous problem. In this study, we present a method of scanning the galleries of Monochamus alternatus using CT (computed tomography) technology to obtain their systematic structure via 3D (three-dimensional) reconstruction, so as to clarify the gallery types and their structural parameters. TLC (thin-layer chromatography) scanning on wood segments damaged by M. alternatus was performed using a 128-row spiral CT GE Revolution EVO to obtain 64-layer CT scanned images. From the scanned images, we were able to clearly identify the beetle larvae and their galleries. The galleries were clearly delineated from the peripheral xylem, except for parts that were blocked by a frass–feces mixture, which were slightly blurred. Three-dimensional reconstruction of the galleries showed that most of the gallery types were C-shaped, and a few were S-shaped or Y-shaped. There was only one larva per gallery, and the galleries were separate. The vicinity of the entrance hole and the anterior part of the pupal chamber were blocked with a frass–feces mixture. There were no significant differences among the galleries’ parameters, such as the width of the entrance holes, tunneling depth, vertical length, blockage length and volume, total length of the galleries, and boring volume. With MIMICS (Materialise’s interactive medical image control system) image processing software, the images of each layer were made into a composite image, providing an effective way to visualize the 3D distribution of galleries. Using the methodology outlined in this study, both a single gallery structure and the spatial distribution of multiple galleries of M. alternatus can be shown, and the specific parameters of galleries can also be accurately calculated, which provides new ideas and methods for carrying out ecological and scientific research and precise prevention and control techniques of M. alternatus.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.