新闻

We are delighted to announce that our paper, “Harvesting Sequence Optimization for Clustered Strawberries Using a Fruit-Level Harvesting Difficulty Score,” has been accepted for publication in Computers and Electronics in Agriculture.

The paper is led by Lin Yang (杨琳), a Master’s student and the first author. The co-authors are Shimin Hu, Meili Sun, and Ya Xiong. This research was conducted by the Intelligent Equipment Research Center at the Beijing Academy of Agriculture and Forestry Sciences, in collaboration with the College of Information Science and Engineering at Shandong Agricultural University.

We are excited to share that our team has begun field testing WeedHitter 2, the latest version of our laser weeding robot.

WeedHitter 2 features a new modular design that brings the platform closer to an industrial-ready system. The redesigned architecture supports more efficient integration, testing, maintenance, and future upgrades as we continue moving the technology from research prototypes toward practical agricultural deployment.

We are pleased to announce that two papers led by our master’s students, Changyou Miao and Li Teng, have been accepted for presentation at IEEE/RSJ IROS 2026. Both works address

A paper led by PhD student Meili Sun has been published in Artificial Intelligence in Agriculture, titled “Vision-based early fault diagnosis and self-recovery for strawberry harvesting robots”. The study presents

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

We are delighted to announce the acceptance of a paper to IEEE ICRA 2026, marking a remarkable achievement led by Xi Chen, an undergraduate intern from China Agricultural University. Co-first

A paper led by master student Nengwei Yang, titled “PHDT-DETR: A Lightweight End-to-End Detector for On-Device Truss Tomato Detection in Greenhouses,” has been published in Smart Agricultural Technology. The study proposes the PHDT-DETR, a streamlined detection model that maintains accuracy while being efficient enough to run in real-time on embedded devices within greenhouses, facilitating scalable robotic scouting and harvesting.

A paper led by PhD student Shimin Hu, titled “Calibration-enhanced multi-view RGB-D vision for robust recognition and 3D localization of strawberries under occlusions,” has been accepted by Computers and Electronics in Agriculture.

A paper led by PhD student Meili Sun, titled “Light-resilient visual regression of strawberry ripeness for robotic harvesting,” has been accepted for publication in Computers and Electronics in Agriculture. The research introduces a robust visual regression model that maintains high accuracy in judging strawberry ripeness under varying greenhouse light conditions.

On October 20, 2025, the IROS Workshop on Agricultural Robotics (Agrirobotics), co-organized by Dr. Ya Xiong alongside leading experts from Wageningen University, University of Lincoln, and Northwest A&F University, was successfully held at the top-tier robotics conference IROS.