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智能化农业装备学报（中英文）

智能化农业装备学报（中英文） ›› 2025, Vol. 6 ›› Issue (1): 15-24.DOI: 10.12398/j.issn.2096-7217.2025.01.002

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基于STM32大蒜联合收获机监测系统设计与试验

王永健1,2(), 丁想1,2, 李骅1,2(), SAMUEL Mbugua Nyambura1,2, 李玉青1,2, 葛艳艳1,2, 仇世龙1,2, 冯学斌1,2   

  1. 1.南京农业大学工学院，江苏 南京，210031
    2.江苏省智能化农业装备重点实验室，江苏 南京，210031
  • 收稿日期:2024-04-30 修回日期:2024-06-21 出版日期:2025-02-15 发布日期:2025-02-15
  • 通讯作者: 李骅
  • 作者简介:王永健，男，1983年生，江苏启东人，博士，副教授；研究方向为智能化种收关键技术。E-mail： yjwang@njau.edu.cn
  • 基金资助:
    国家重点研发计划(2017YFD0701302);江苏省现代农机装备与技术示范推广项目(NJ2021-12)

Design and experiment of STM32-based monitoring system for garlic combine harvester

WANG Yongjian1,2(), DING Xiang1,2, LI Hua1,2(), SAMUEL Mbugua Nyambura1,2, LI Yuqing1,2, GE Yanyan1,2, QIU Shilong1,2, FENG Xuebin1,2   

  1. 1.College of Engineering Nanjing Agricultural University，Nanjing 210031，China
    2.Key Laboratory of Intelligent Agricultural Equipment in Jiangsu Province，Nanjing 210031，China
  • Received:2024-04-30 Revised:2024-06-21 Online:2025-02-15 Published:2025-02-15
  • Contact: LI Hua
  • About author:WANG Yongjian，E-mail： yjwang@njau.edu.cn
  • Supported by:
    National Key R&D Program of China(2017YFD0701302);Jiangsu Province Demonstration and Promotion Project of Modern Agricultural Machinery Equipment and Technology(NJ2021-12)

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摘要：

大蒜联合收获机已基本实现了大蒜的机械化收获，但其智能化水平较低，在作业过程中易发生偏航、溢仓及堵塞等问题，影响整机的作业质量和收获效率。针对这些问题，本研究设计了一套大蒜联合收获机的实时监测系统，能够实时、准确监测作业过程中的偏航、溢仓和堵塞故障问题，并进行语音播报提醒。该系统主要包括对行辅助模块、产量监测模块、故障诊断模块以及STM32单片机等。其中，对行辅助使用MPU6050陀螺仪监测收获机航向信息，与设定的航向角预警、报警阈值进行比较，判断收获机是否偏离正常作业路线；产量监测将单粒大蒜质量与统计的大蒜总数相乘计算出已收获的蒜头总质量，并与设定的满仓阈值进行比较，判断收集仓是否满仓；故障诊断通过霍尔传感器收集夹持输送机构从动轮的转速，并与设定阈值进行比较，判断是否发生堵塞。通过室内调试，对行辅助模块最大误差值为1.3°，最小误差值为0.2°；产量监测模块监测计数准确率为96.7%；故障诊断模块识别转速平均误差值为0.8 r/min，误差率为0.4%，能够准确监测夹持装置堵塞时从动轮转速的变化状况。田间试验结果表明设计的监测系统具备可行性、准确性和稳定性。通过该研究，本监测系统可以长时间稳定工作，并在大蒜收获机出现偏航、溢仓及堵塞故障时，及时发出报警信息，能够显著改善传统大蒜收获机的作业质量，提升作业效率。

关键词: 大蒜, 收获机, 对行辅助, 实时监测, 阈值

Abstract:

The garlic combine harvester has essentially realized the mechanized harvesting of garlic. However， its intelligence level is relatively low. During the operation， problems such as yaw， overflow and blockage are prone to occur， which affect the operation quality and harvesting efficiency of the whole machine. To address these issues， this paper presents an STM32-Based real-time monitoring system for garlic combine harvesters， which can monitor the yawing， overflow， and blockage failure problems in real-time with high accuracy， and can also provide voice broadcast reminders. The system comprised a row assist modules， a yield monitoring module， a fault diagnosis module and an STM32 microcontroller. The row assist modules utilized an MPU6050 gyroscope to ascertain the direction of the harvester， which was then compared with the set direction angle warning and alarm threshold to determine whether the harvester had deviated from the normal operational route. The yield monitoring module multiplied the weight of a single garlic by the total number of garlic counts to calculate the total weight of the harvested garlic， and compared it with the set threshold of the full bin to ascertain whether the collection bin was indeed full. Fault diagnosis was performed by the Hall sensor， which collected the rotational speed of the follower wheel of the clamping and conveying mechanism and compared it with the set threshold to judge whether a blockage occurrs. The maximum error value of the row assist module， as determined through indoor commissioning， was 1.3°. The minimum error value was 0.2°. The yield detection module monitoring count accuracy was 96.7%. The fault diagnosis module identified the average error value of rotational speed as 0.8 r/min， with an error rate of 0.4%. This enabled the module to accurately monitor the follower wheel rotational speed changes when the clamping device was blocked. The results of the field test demonstrate that the designed monitoring system is feasible， accurate and stable. The findings of this study indicate that the proposed monitoring system can operate reliably over an extended period， issuing timely alerts when the garlic harvester is exhibiting signs of yawing， overflowing， and blocking issues. This could markedly enhance the operational quality and efficiency of traditional garlic harvesters.

Key words: garlic, harvester, row assist, real-time monitoring, threshold

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