Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model
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| Xuất bản năm: | Agriculture vol. 15, no. 3 (2025), p. 237 |
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| Tác giả chính: | |
| Tác giả khác: | , , , |
| Được phát hành: |
MDPI AG
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| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Bài tóm tắt: | 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. |
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| số ISSN: | 2077-0472 |
| DOI: | 10.3390/agriculture15030237 |
| Nguồn: | Agriculture Science Database |