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On-Line Detection Method and Device for Moisture Content Measurement of Bales in a Square Baler

Author

Listed:
  • Huaiyu Liu

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Zhijun Meng

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Anqi Zhang

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yue Cong

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xiaofei An

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Weiqiang Fu

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Guangwei Wu

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yanxin Yin

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Chengqian Jin

    (Nanjing Research Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)

Abstract

Aiming to address the problems of low detection accuracy and poor stability due to the weak anti-interference ability of the bridge circuit and operational amplifier circuit, and the influence on the capacitance of the bulk density and temperature of the straw bale, an on-line detection device for the moisture content of straw bales in a square baler was developed based on the capacitance method. The device integrates a capacitance sensor, pressure sensor, and temperature sensor. The optimal structure size of the capacitor plate was determined through the simulation test of the capacitor sensor plate structure. A moisture content monitoring system based on the MATLAB language is built, and the moisture content detection model was constructed based on the backpropagation neural network (BPNN) algorithm. Finally, a test bench for a square baling machine was designed, and a performance verification test of the moisture content detection device was carried out. The simulation results of the capacitor plate show that when the length, width, and spacing of the capacitor plate are 148.6, 47.7, and 5.1 mm, respectively, the detection sensitivity of the capacitor plate is the highest. The modeling results show that the R 2 , RMSE , and RPD of the BPNN model are 0.986, 0.008998, and 5.99, respectively, with solid data fitting ability and high prediction accuracy. The bench test results show that for the samples having moisture content between 13.1 and 28.04%, the coefficient of determination R 2 of the fitting curve between the predicted value of moisture content and the actual value is 0.949. The relative error range of the predicted value of moisture content is −6.51–8.66%, and the absolute error range is −1.63–1.72%. The on-line detection device for moisture content of straw bales has good accuracy and stability.

Suggested Citation

  • Huaiyu Liu & Zhijun Meng & Anqi Zhang & Yue Cong & Xiaofei An & Weiqiang Fu & Guangwei Wu & Yanxin Yin & Chengqian Jin, 2022. "On-Line Detection Method and Device for Moisture Content Measurement of Bales in a Square Baler," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1183-:d:883715
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    References listed on IDEAS

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    1. Wenguang Yu & Guofeng Guan & Jingchao Li & Qi Wang & Xiaohan Xie & Yu Zhang & Yujuan Huang & Xinliang Yu & Chaoran Cui & Benjamin Miranda Tabak, 2021. "Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network," Complexity, Hindawi, vol. 2021, pages 1-17, January.
    2. Ohana-Levi, Noa & Ben-Gal, Alon & Munitz, Sarel & Netzer, Yishai, 2022. "Grapevine crop evapotranspiration and crop coefficient forecasting using linear and non-linear multiple regression models," Agricultural Water Management, Elsevier, vol. 262(C).
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    Cited by:

    1. Ling Ren & Shuang Wang & Bin Hu & Tao Li & Ming Zhao & Yuquan Zhang & Miao Yang, 2023. "Seed State-Detection Sensor for a Cotton Precision Dibble," Agriculture, MDPI, vol. 13(8), pages 1-18, July.

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