EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments
Accurate detection of sugarcane nodes in natural environments is crucial for realizing intelligent sugarcane cutting and precise planting localization. However, current sugarcane node detection models often face challenges such as large parameter sizes, poor adaptability to deployment environments,...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1626725/full |
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| author | Zhenhui Zheng Zhenhui Zheng Zhenhui Zheng Lijiao Wei Lijiao Wei Kangmin Lin Weihua Huang Shuo Wang Dongjie Du Tao Wu |
| author_facet | Zhenhui Zheng Zhenhui Zheng Zhenhui Zheng Lijiao Wei Lijiao Wei Kangmin Lin Weihua Huang Shuo Wang Dongjie Du Tao Wu |
| author_sort | Zhenhui Zheng |
| collection | DOAJ |
| description | Accurate detection of sugarcane nodes in natural environments is crucial for realizing intelligent sugarcane cutting and precise planting localization. However, current sugarcane node detection models often face challenges such as large parameter sizes, poor adaptability to deployment environments, and limited real-world detection accuracy. To address these challenges, this research proposes a high-precision and lightweight EdgeSugarcane detection model. Firstly, based on YOLOv8, an improved EdgeSugarcane model is proposed. By introducing an interactive attention mechanism across channel and spatial dimensions, the model’s ability to represent node features is enhanced. Then, combined with TensorRT acceleration and optimization, the optimal FP16 quantization deployment scheme is proposed. Finally, end-to-end deployment is implemented on the NVIDIA Orin NX edge device, and its performance and resource consumption in practical applications are analyzed in depth. The experimental results show that EdgeSugarcane achieves a precision of 0.935, a recall of 0.8, and a mAP of 0.87 on the test set, with a model size of 89.9 MB. Compared to YOLOv8, the mAP is improved by 0.6%, and the inference speed is increased by 44%. With lossless precision, the inference time after FP16 quantization is only 1.9ms, a 3.3-fold improvement compared to before optimization, and the model size changes very little. On the NVIDIA Orin NX device, the single-frame inference, pre-processing, and post-processing times are 1.5ms, 60.6ms, and 4.4ms, respectively. The EdgeSugarcane model demonstrates excellent real-time performance and high accuracy under natural field conditions, offering a viable solution for integration into edge-based robotic systems for intelligent sugarcane cutting and precision planting. |
| format | Article |
| id | doaj-art-56b73ccf11a8467b9cc53dd478ad9ac2 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-56b73ccf11a8467b9cc53dd478ad9ac22025-08-20T03:17:23ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.16267251626725EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environmentsZhenhui Zheng0Zhenhui Zheng1Zhenhui Zheng2Lijiao Wei3Lijiao Wei4Kangmin Lin5Weihua Huang6Shuo Wang7Dongjie Du8Tao Wu9College of Engineering, South China Agricultural University, Guangzhou, ChinaAgricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong, ChinaKey Laboratory of Agricultural Equipment for Tropical Crops, Ministry of Agriculture and Rural Affairs, Zhanjiang, Guangdong, ChinaAgricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong, ChinaKey Laboratory of Agricultural Equipment for Tropical Crops, Ministry of Agriculture and Rural Affairs, Zhanjiang, Guangdong, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaAgricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong, ChinaAgricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong, ChinaAgricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, Guangdong, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaAccurate detection of sugarcane nodes in natural environments is crucial for realizing intelligent sugarcane cutting and precise planting localization. However, current sugarcane node detection models often face challenges such as large parameter sizes, poor adaptability to deployment environments, and limited real-world detection accuracy. To address these challenges, this research proposes a high-precision and lightweight EdgeSugarcane detection model. Firstly, based on YOLOv8, an improved EdgeSugarcane model is proposed. By introducing an interactive attention mechanism across channel and spatial dimensions, the model’s ability to represent node features is enhanced. Then, combined with TensorRT acceleration and optimization, the optimal FP16 quantization deployment scheme is proposed. Finally, end-to-end deployment is implemented on the NVIDIA Orin NX edge device, and its performance and resource consumption in practical applications are analyzed in depth. The experimental results show that EdgeSugarcane achieves a precision of 0.935, a recall of 0.8, and a mAP of 0.87 on the test set, with a model size of 89.9 MB. Compared to YOLOv8, the mAP is improved by 0.6%, and the inference speed is increased by 44%. With lossless precision, the inference time after FP16 quantization is only 1.9ms, a 3.3-fold improvement compared to before optimization, and the model size changes very little. On the NVIDIA Orin NX device, the single-frame inference, pre-processing, and post-processing times are 1.5ms, 60.6ms, and 4.4ms, respectively. The EdgeSugarcane model demonstrates excellent real-time performance and high accuracy under natural field conditions, offering a viable solution for integration into edge-based robotic systems for intelligent sugarcane cutting and precision planting.https://www.frontiersin.org/articles/10.3389/fpls.2025.1626725/fullsugarcaneYOLOv8TensorRTedge computinglightweight |
| spellingShingle | Zhenhui Zheng Zhenhui Zheng Zhenhui Zheng Lijiao Wei Lijiao Wei Kangmin Lin Weihua Huang Shuo Wang Dongjie Du Tao Wu EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments Frontiers in Plant Science sugarcane YOLOv8 TensorRT edge computing lightweight |
| title | EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments |
| title_full | EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments |
| title_fullStr | EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments |
| title_full_unstemmed | EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments |
| title_short | EdgeSugarcane: a lightweight high-precision method for real-time sugarcane node detection in edge computing environments |
| title_sort | edgesugarcane a lightweight high precision method for real time sugarcane node detection in edge computing environments |
| topic | sugarcane YOLOv8 TensorRT edge computing lightweight |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1626725/full |
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