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Full-condition monitoring and intelligent yield prediction and decisio-nmaking technology for wheat combine harvesters.

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Title: Full-condition monitoring and intelligent yield prediction and decisio-nmaking technology for wheat combine harvesters.
Authors: Weipeng Zhang1 zhangwellp@163.com, Hongze Guo1 johnguohz@163.com, Bo Zhao1 zhaoboshi@126.com, Liming Zhou1 haibo1129@163.com, Fengzhu Wang1 wangfengzhu1@126.com, Dongyang Wang2 zpt_wdy@szpu.edu.cn, Yangchun Liu1 lyc327@163.com
Source: International Journal of Agricultural & Biological Engineering. Dec2025, Vol. 18 Issue 6, p202-211. 10p.
Subjects: Combines (Agricultural machinery), Controller area network (Computer network), High technology, Flow sensors, Agricultural forecasts, Fault diagnosis, Precision farming
Abstract: Against the backdrop of precision agriculture and the development of intelligent agricultural machinery, current domestic monitoring systems for wheat combine harvesters are plagued by limited functionality, low intelligence, significant errors in parameter monitoring, and yield estimation results prone to inaccuracies. Specifically, they lag behind mature international systems in terms of fault warning accuracy, data transmission efficiency, and yield visualization capabilities. This study seeks to realize comprehensive and precise monitoring, reliable fault early warning, and intelligent yield prediction for wheat combine harvesters across all operating conditions. To this end, it innovatively adopts CAN bus integration technology and impulse-type grain flow sensors to develop a comprehensive system for monitoring the operational status and warning faults of wheat combine harvesters, which covers the entire operational process. By integrating GPS positioning, multi-sensor parameter acquisition, and intelligent analysis modules through CAN bus integration, the system enables unified monitoring of geographic information, operational data, cleaning loss, and fault status. Additionally, it incorporates a yield measurement module based on an impulse-type grain flow sensor to generate the real-time yield distribution maps. Field experiments demonstrate that the system achieves an alarm accuracy of 97.3%, controls the fuel consumption measurement error within 5%, and limits the relative error of yield measurement accuracy to no more than 4%. Notably, the impulse-type grain flow sensor exhibits stable static detection accuracy and rapid, precise dynamic measurement performance--laying a solid foundation for the automation and intelligent advancement of combine harvester technologies. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Full-condition monitoring and intelligent yield prediction and decisio-nmaking technology for wheat combine harvesters.
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  Data: <searchLink fieldCode="AR" term="%22Weipeng+Zhang%22">Weipeng Zhang</searchLink><relatesTo>1</relatesTo><i> zhangwellp@163.com</i><br /><searchLink fieldCode="AR" term="%22Hongze+Guo%22">Hongze Guo</searchLink><relatesTo>1</relatesTo><i> johnguohz@163.com</i><br /><searchLink fieldCode="AR" term="%22Bo+Zhao%22">Bo Zhao</searchLink><relatesTo>1</relatesTo><i> zhaoboshi@126.com</i><br /><searchLink fieldCode="AR" term="%22Liming+Zhou%22">Liming Zhou</searchLink><relatesTo>1</relatesTo><i> haibo1129@163.com</i><br /><searchLink fieldCode="AR" term="%22Fengzhu+Wang%22">Fengzhu Wang</searchLink><relatesTo>1</relatesTo><i> wangfengzhu1@126.com</i><br /><searchLink fieldCode="AR" term="%22Dongyang+Wang%22">Dongyang Wang</searchLink><relatesTo>2</relatesTo><i> zpt_wdy@szpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yangchun+Liu%22">Yangchun Liu</searchLink><relatesTo>1</relatesTo><i> lyc327@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Agricultural+%26+Biological+Engineering%22">International Journal of Agricultural & Biological Engineering</searchLink>. Dec2025, Vol. 18 Issue 6, p202-211. 10p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Combines+%28Agricultural+machinery%29%22">Combines (Agricultural machinery)</searchLink><br /><searchLink fieldCode="DE" term="%22Controller+area+network+%28Computer+network%29%22">Controller area network (Computer network)</searchLink><br /><searchLink fieldCode="DE" term="%22High+technology%22">High technology</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+sensors%22">Flow sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+forecasts%22">Agricultural forecasts</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Against the backdrop of precision agriculture and the development of intelligent agricultural machinery, current domestic monitoring systems for wheat combine harvesters are plagued by limited functionality, low intelligence, significant errors in parameter monitoring, and yield estimation results prone to inaccuracies. Specifically, they lag behind mature international systems in terms of fault warning accuracy, data transmission efficiency, and yield visualization capabilities. This study seeks to realize comprehensive and precise monitoring, reliable fault early warning, and intelligent yield prediction for wheat combine harvesters across all operating conditions. To this end, it innovatively adopts CAN bus integration technology and impulse-type grain flow sensors to develop a comprehensive system for monitoring the operational status and warning faults of wheat combine harvesters, which covers the entire operational process. By integrating GPS positioning, multi-sensor parameter acquisition, and intelligent analysis modules through CAN bus integration, the system enables unified monitoring of geographic information, operational data, cleaning loss, and fault status. Additionally, it incorporates a yield measurement module based on an impulse-type grain flow sensor to generate the real-time yield distribution maps. Field experiments demonstrate that the system achieves an alarm accuracy of 97.3%, controls the fuel consumption measurement error within 5%, and limits the relative error of yield measurement accuracy to no more than 4%. Notably, the impulse-type grain flow sensor exhibits stable static detection accuracy and rapid, precise dynamic measurement performance--laying a solid foundation for the automation and intelligent advancement of combine harvester technologies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Agricultural & Biological Engineering is the property of International Journal of Agricultural & Biological Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
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      – Type: doi
        Value: 10.25165/j.ijabe.20251806.8780
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 202
    Subjects:
      – SubjectFull: Combines (Agricultural machinery)
        Type: general
      – SubjectFull: Controller area network (Computer network)
        Type: general
      – SubjectFull: High technology
        Type: general
      – SubjectFull: Flow sensors
        Type: general
      – SubjectFull: Agricultural forecasts
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Precision farming
        Type: general
    Titles:
      – TitleFull: Full-condition monitoring and intelligent yield prediction and decisio-nmaking technology for wheat combine harvesters.
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            NameFull: Weipeng Zhang
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            NameFull: Hongze Guo
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            NameFull: Bo Zhao
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            NameFull: Liming Zhou
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            NameFull: Fengzhu Wang
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            NameFull: Dongyang Wang
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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              Value: 18
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            – TitleFull: International Journal of Agricultural & Biological Engineering
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