Slim-Sugarcane: A Lightweight and High-Precision Method for Sugarcane Node Detection and Edge Deployment in Natural Environments

Lijiao Wei¹Shuo Wang¹Xinwei Liang²Dongjie Du¹Xinyi Huang³Ming Li¹Yuangang Hua¹Weihua Huang¹Zhenhui Zheng^1,4*

• ¹Chinese Academy of Tropical Agricultural Sciences Agricultural Machinery Research Institute, Zhanjiang, China
• ²College of Engineering, South China Agricultural University, Guangzhou, China
• ³School of Information Technology & Engineering, Guangzhou College of Commerce, Guangzhou, China
• ⁴Institute of Agricultural Machinery, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang, China

Accurate detection of sugarcane nodes in complex field environments is a critical prerequisite for intelligent seed cutting and automated planting. However, existing detection methods often suffer from large model sizes and suboptimal performance, limiting their applicability on resource-constrained edge devices. To address these challenges, we propose Slim-Sugarcane, a lightweight and high-precision node detection framework optimized for real-time deployment in natural agricultural settings. Built upon YOLOv8, our model integrates GSConv, a hybrid convolution module combining group and spatial convolutions, to significantly reduce computational overhead while maintaining detection accuracy. We further introduce a Cross-Stage Local Network module featuring a single-stage aggregation strategy, which effectively minimizes structural redundancy and enhances feature representation. The proposed framework is optimized with TensorRT and deployed using FP16 quantization on the 2 NVIDIA Jetson Orin NX platform to ensure real-time performance under limited hardware conditions. Experimental results demonstrate that Slim-Sugarcane achieves a precision of 0.922, recall of 0.802, and mean average precision of 0.852, with an inference latency of only 60.1 ms and a GPU memory footprint of 1434 MB. The proposed method exhibits superior accuracy and computational efficiency compared to existing approaches, offering a promising solution for precision agriculture and intelligent sugarcane cultivation.