YOLOv8n压缩模型在STM32U5上实现低功耗农田杂草实时检测
Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture
- 采用结构化剪枝、整数量化与图像分辨率缩放压缩模型
- 单次推理仅耗电51.8mJ，支持实时检测
- 适用于电池供电的边缘农业设备
杂草全球范围内显著降低作物产量，传统防治依赖化学除草剂，存在环境污染与抗药性风险。精准除草结合计算机视觉与机器学习提供环保替代方案，但常受限于高功耗计算平台。本文基于YOLOv8n目标检测器，在STM32U575ZI微控制器上构建低功耗边缘AI系统，通过结构化剪枝、整数量化及输入图像分辨率缩放等压缩技术，满足严苛硬件约束。模型在包含74种植物的CropAndWeed数据集上训练与评估，实现检测精度与效率的平衡。系统支持实时、原位杂草检测，单次推理能耗低至51.8mJ，可拓展部署于电力受限的农业环境。
原文摘要 · Abstract (English)
Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture. Traditional weed management methods, primarily relying on chemical herbicides, risk environmental contamination and lead to the emergence of herbicide-resistant species. Precision weeding, leveraging computer vision and machine learning methods, offers a promising eco-friendly alternative but is often limited by reliance on high-power computational platforms. This work presents an optimized, low-power edge AI system for weeds detection based on the YOLOv8n object detector deployed on the STM32U575ZI microcontroller. Several compression techniques are applied to the detection model, including structured pruning, integer quantization and input image resolution scaling in order to meet strict hardware constraints. The model is trained and evaluated on the CropAndWeed dataset with 74 plant species, achieving a balanced trade-off between detection accuracy and efficiency. Our system supports real-time, in-situ weeds detection with a minimal energy consumption of 51.8mJ per inference, enabling scalable deployment in power-constrained agricultural environments.
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