Abstract
Weed detection is a critical component of precision agriculture, facilitating targeted herbicide application and reducing environmental impact. However, deploying accurate object detection models on resource-limited platforms remains challenging, particularly when differentiating visually similar weed species commonly encountered in plant phenotyping applications. In this work, we investigate Channel-wise Knowledge Distillation (CWD) and Masked Generative Distillation (MGD) to enhance the performance of lightweight models for real-time smart spraying systems. Utilizing YOLO11x as the teacher model and YOLO11n as both reference and student, both CWD and MGD effectively transfer knowledge from the teacher to the student model. Our experiments, conducted on a real-world dataset comprising sugar beet crops and four weed types (Cirsium, Convolvulus, Fallopia, and Echinochloa), consistently show increased AP50 across all classes. The distilled CWD student model achieves a notable improvement of 2.5% and MGD achieves 1.9% in mAP50 over the baseline without increasing model complexity. Additionally, we validate real-time deployment feasibility by evaluating the student YOLO11n model on Jetson Orin Nano and Raspberry Pi 5 embedded devices, performing five independent runs to evaluate performance stability across random seeds. These findings confirm CWD and MGD as an effective, efficient, and practical approach for improving deep learning-based weed detection accuracy in precision agriculture and plant phenotyping scenarios.
Abstract (translated)
杂草检测是精准农业中的一个关键组成部分，它有助于实现有针对性的除草剂应用并减少对环境的影响。然而，在资源受限平台上部署准确的对象检测模型仍然具有挑战性，尤其是在区分植物表型应用中常见的视觉相似杂草种类时。在这项工作中，我们研究了通道级知识蒸馏（CWD）和掩码生成式蒸馏（MGD），以增强轻量级模型在实时智能喷洒系统中的性能。使用YOLO11x作为教师模型，并将YOLO11n同时用作参考和学生模型，这两种方法都能够有效地从教师模型向学生模型转移知识。 我们的实验是在一个包含甜菜作物及四种杂草类型（蓟、旋花、豚草和千金子）的真实世界数据集上进行的，结果表明所有类别的平均精度（AP50）均有所提高。经过蒸馏处理后的CWD学生模型在mAP50方面比基准模型提高了2.5%，而MGD则提高了1.9%，同时没有增加模型复杂度。 此外，为了验证实时部署的可能性，我们在Jetson Orin Nano和Raspberry Pi 5嵌入式设备上评估了YOLO11n的学生模型，并进行了五次独立运行以评估不同随机种子下的性能稳定性。这些发现证实了CWD和MGD是一种有效、高效且实用的方法，能够提高基于深度学习的杂草检测精度，在精准农业及植物表型应用中具有重要意义。
URL
https://arxiv.org/abs/2507.12344
