Authors

Abstract

The efficiency and quality of tobacco leaf harvesting are crucial for the economic performance of the tobacco industry. To enhance harvesting efficiency, a non-destructive tobacco leaf harvesting robot based on machine vision and robotics technology was developed. Experimental evaluations of key components demonstrated that the biomimetic flexible gripper based on the fin ray effect has good stiffness when the clamping force is 2.5 N, ensuring stable subsequent harvesting and collection of tobacco leaves. The introduction of a 6+1-axis robotic arm significantly expands the working range compared to the original 6-axis design, effectively covering the height of the tobacco column. The robotic arm's speed notably affects harvesting time (P < 0.001), with 1.2 m/s identified as optimal for balancing recognition efficiency and success rates. Additionally, exposure time plays a critical role in success rates (P < 0.001), achieving peaks of 90.00% in the morning and 83.33% in the afternoon at 40000 μs. These advancements enhance tobacco harvesting technology and provide valuable insights for intelligent crop harvesting.

Abstract in Chinese

烟叶采摘的效率和质量对烟草行业的经济效益至关重要。为了提高采摘效率，基于机器视觉和机器人技术开发了一种无损烟叶采摘机器人。关键部件的实验评估表明，基于鳍条效应的仿生柔性夹爪在夹紧力为2.5 N时具有良好的刚度确保了后续烟叶采摘和回收的稳定性。引入的6+1轴机械臂相比原有的6轴设计，显著扩展了工作范围，有效覆盖了烟草柱的高度。机械臂的速度显著影响采摘时间（P < 0.001），1.2 m/s的速度被确定为平衡识别效率和成功率的最佳值。此外，曝光时间对成功率也有关键作用（P < 0.001），上午和下午在40000 μs时成功率分别达到90.00%和83.33%。这些进展提升了烟叶采摘技术，并为农作物智能采摘提供了借鉴。