Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model
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| Publicado en: | Agriculture vol. 15, no. 3 (2025), p. 237 |
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| Autor Principal: | |
| Outros autores: | , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3165754089 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-0472 | ||
| 024 | 7 | |a 10.3390/agriculture15030237 |2 doi | |
| 035 | |a 3165754089 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231331 |2 nlm | ||
| 100 | 1 | |a Liang, Yun | |
| 245 | 1 | |a Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Harvesting | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Citrus fruits | ||
| 653 | |a Segmentation | ||
| 653 | |a Optimization | ||
| 653 | |a Workflow | ||
| 653 | |a Localization | ||
| 653 | |a Clustering | ||
| 653 | |a Fruits | ||
| 653 | |a Distillation | ||
| 653 | |a Localization method | ||
| 653 | |a Embedded systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Picking | ||
| 653 | |a Labor shortages | ||
| 653 | |a Design | ||
| 653 | |a Algorithms | ||
| 653 | |a Apples | ||
| 653 | |a Object recognition | ||
| 653 | |a Real time | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Jiang, Weipeng | |
| 700 | 1 | |a Liu, Yunfan | |
| 700 | 1 | |a Wu, Zihao | |
| 700 | 1 | |a Zheng, Run | |
| 773 | 0 | |t Agriculture |g vol. 15, no. 3 (2025), p. 237 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165754089/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165754089/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165754089/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |