Hao Ma, Yulong Ding, Hongwei Cui, Jiangtao Ji, Xin Jin, Tianhang Ding, Jiaoling Wang
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引用次数: 0
Abstract
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.
针对双孢蘑菇人工采收效率低、危害大、成本高等问题,根据双孢蘑菇的农艺特性和形态特征,设计了一种基于机器视觉的双孢蘑菇智能采收装置。该装置主要由机架、摄像机、桁架式机械臂、柔性机械手和控制系统组成。采用FES-YOLOv5s深度学习目标检测模型对双孢蘑菇进行准确识别和定位。采集控制系统以Jetson Orin Nano为主控制器,采用s曲线加减速电机控制算法。该算法基于识别和定位结果,控制机械臂和柔性机械手采集双孢蘑菇。为了确认振动对采收过程的影响,采用梯形加减速电机控制算法和s曲线加减速电机控制算法进行了步进电机驱动试验。试验结果表明,s曲线加减速电机控制算法在减振和重复定位精度方面具有优异的性能。以识别准确率、收获成功率和毁伤率为评价指标,对智能收获装置的识别效率和收获效果进行了测试。结果表明,双孢蘑菇识别算法的平均识别准确率为96.72%,平均漏检率为2.13%,假检率为1.72%。智能采收装置的采收成功率为94.95%,平均伤害率为2.67%,平均采收率为87.38%。这些结果满足了双孢蘑菇智能采收的要求,为双孢蘑菇工业化生产中智能采收机器人的发展提供了参考。
期刊介绍:
Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.