A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies

Jin Lu (), Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao and Minjie Zhang
Additional contact information
Jin Lu: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China
Zhongji Cao: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China
Jin Wang: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China
Zhao Wang: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China
Jia Zhao: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China
Minjie Zhang: School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, 618 Chang’an West St., Xi’an 710121, China

Agriculture, 2025, vol. 15, issue 15, 1-30

Abstract: During the automated picking of table grapes, the automatic recognition and segmentation of grape pedicels, along with the positioning of picking points, are vital components for all the following operations of the harvesting robot. In the actual scene of a grape plantation, however, it is extremely difficult to accurately and efficiently identify and segment grape pedicels and then reliably locate the picking points. This is attributable to the low distinguishability between grape pedicels and the surrounding environment such as branches, as well as the impacts of other conditions like weather, lighting, and occlusion, which are coupled with the requirements for model deployment on edge devices with limited computing resources. To address these issues, this study proposes a novel picking point localization method for table grapes based on an instance segmentation network called Progressive Global-Local Structure-Sensitive Segmentation (PGSS-YOLOv11s) and a simple combination strategy of morphological operators. More specifically, the network PGSS-YOLOv11s is composed of an original backbone of the YOLOv11s-seg, a spatial feature aggregation module (SFAM), an adaptive feature fusion module (AFFM), and a detail-enhanced convolutional shared detection head (DE-SCSH). And the PGSS-YOLOv11s have been trained with a new grape segmentation dataset called Grape-⊥, which includes 4455 grape pixel-level instances with the annotation of ⊥-shaped regions. After the PGSS-YOLOv11s segments the ⊥-shaped regions of grapes, some morphological operations such as erosion, dilation, and skeletonization are combined to effectively extract grape pedicels and locate picking points. Finally, several experiments have been conducted to confirm the validity, effectiveness, and superiority of the proposed method. Compared with the other state-of-the-art models, the main metrics F 1 score and mask mAP@0.5 of the PGSS-YOLOv11s reached 94.6% and 95.2% on the Grape-⊥ dataset, as well as 85.4% and 90.0% on the Winegrape dataset. Multi-scenario tests indicated that the success rate of positioning the picking points reached up to 89.44%. In orchards, real-time tests on the edge device demonstrated the practical performance of our method. Nevertheless, for grapes with short pedicels or occluded pedicels, the designed morphological algorithm exhibited the loss of picking point calculations. In future work, we will enrich the grape dataset by collecting images under different lighting conditions, from various shooting angles, and including more grape varieties to improve the method’s generalization performance.

Keywords: grape harvesting; deep learning; instance segmentation; picking point positioning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/15/1622/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/15/1622/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:15:p:1622-:d:1710821

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().