Two Papers Accepted: Active Obstacle Separation and Real-Time Strawberry Grading

We are pleased to announce that two papers led by our master’s students have been accepted.

Quan Zhao (first author) and colleagues present a VLM-driven framework for active obstacle separation in robotic harvesting. The work achieved 89.4% clearing success in simulation and 83.9% in real-robot field trials. Accepted by Smart Agricultural Technology.

Jinshan Zhen (first author) and colleagues present a real-time RGB-D grading framework for strawberries under occlusions. The multi-attribute classifier achieved 93.65% overall accuracy with 29.7 FPS inference. Accepted by Smart Agricultural Technology.

Congratulations to both teams! 🎉

我实验室两位硕士研究生作为第一作者的论文近期被国际期刊 Smart Agricultural Technology 正式录用。

赵权（第一作者）及合作者提出一种基于视觉语言模型的主动清障框架，仿真环境清障成功率达89.4%，真实机器人试验达83.9%。

甄金山（第一作者）及合作者提出一种遮挡环境下草莓实时分级框架，多属性分类器整体准确率达93.65%，推理速度29.7 FPS。

祝贺两位同学及合作团队！ 🎉