数字农科院2.0

Digital twin-driven system for efficient tomato harvesting in greenhouses

文献类型： 外文期刊

作者： Yining Lang;Yanqi Zhang;Tan Sun;Xiujuan Chai;Ning Zhang

作者机构：

关键词： Decision;Digital-twin;Greenhouse;Harvest;Reinforcement-learning;Tomato

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2025 年 236 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Efficient and low-damage harvesting remains a major challenge in modern greenhouse tomato production, particularly in dense planting environments. To address limitations such as restricted camera views, occluded fruits, and complex fruiting patterns, our study presents a digital twin-driven system for intelligent tomato harvesting. Using a slidable depth camera mounted on the robot, we reconstruct a high-fidelity 3D digital twin of the greenhouse that accurately captures the spatial distribution and growth states of tomatoes. Based on this virtual environment, a learning-based framework is developed to optimize harvesting strategies, including robot positioning, arm trajectory planning, fruit selection priority, and adaptive operation modes. The proposed system integrates both a complete algorithmic workflow and a practical hardware platform. Experimental results show that our method significantly improves harvesting performance, reducing the average harvesting time by 34.95% (to 7.4 s per fruit), arm movement distance by 20.93%, and collision occurrences by 45.16%. While tailored for tomato harvesting, this framework demonstrates strong potential for generalization to other greenhouse crops in precision agriculture.

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