Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model

Yun Liang (), Weipeng Jiang, Yunfan Liu, Zihao Wu and Run Zheng
Additional contact information
Yun Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Weipeng Jiang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yunfan Liu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zihao Wu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Run Zheng: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 3, 1-24

Abstract: The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus picking-point localization, named as CPPL. The CPPL is achieved based on two stages, namely the detection stage and the segmentation stage. For the detection stage, we define the KD-YOLOP to accurately detect citrus fruits to quickly localize the initial picking region. The KD-YOLOP is defined based on a knowledge distillation learning and a model pruning to reduce the computational cost while having a competitive accuracy. For the segmentation stage, we define the RG-YOLO-seg to efficiently segment the citrus branches to compute the picking points. The RG-YOLO-seg is proposed by introducing the RGNet to extract efficient features and using the GSNeck to fuse multi-scale features. Therefore, by using knowledge distillation, model pruning, and a lightweight model for branch segmentation, the proposed CPPL achieves accurate real-time localization of citrus picking points. We conduct extensive experiments to evaluate our method; many results show that the proposed CPPL outperforms the current methods and achieves adequate accuracy. It provides an efficient and robust novel method for real-time citrus harvesting in practical agricultural applications.

Keywords: citrus; automatic picking; picking-point localization; workflow; model optimization (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: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/3/237/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/3/237/ (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:3:p:237-:d:1573755

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 ().