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An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device

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  • Lan Ma

    (College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
    Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, China
    Hunan Key Laboratory of Intelligent Agricultural Machinery Corporation, Changsha 410102, China)

  • Fangping Xie

    (College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
    Hunan Key Laboratory of Intelligent Agricultural Machinery Corporation, Changsha 410102, China)

  • Dawei Liu

    (College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
    Hunan Key Laboratory of Intelligent Agricultural Machinery Corporation, Changsha 410102, China)

  • Xiushan Wang

    (College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
    Hunan Key Laboratory of Intelligent Agricultural Machinery Corporation, Changsha 410102, China)

  • Zhanfeng Zhang

    (Changsha Zichen Technology Development Co., Ltd., Changsha 410221, China)

Abstract

Rice is a widely cultivated food crop worldwide, and threshing is one of the most important operations of combine harvesters in grain production. It is a complex, nonlinear, multi-parameter physical process. The flexible threshing device has unique advantages in reducing the grain damage rate and has already been one of the major concerns in engineering design. Using the measured test database of the flexible threshing test bench, the rotation speed of the threshing cylinder (RS), threshing clearance of the concave sieve (TC), separation clearance of the concave sieve (SC), and feeding quantity (FQ) are used as the input layer. In contrast, the crushing rate (Y P ), impurity rate of the threshed material (Y Z ), and loss rate (Y S ) are used in the output layer. A 4-5-3-3 artificial neural network (ANN) model, with a backpropagation learning algorithm, was developed to predict the threshing performance of the flexible threshing device. Next, we explored the degree to which the inputs affect the outputs. The results showed that the R of the threshing performance model validation set in the hidden layer reached 0.980, and the root mean square error ( R M S E ) and the average absolute error ( M A E ) were less than 0.139 and 0.153, respectively. The built neural network model predicted the performance of the flexible threshing device, and the regression determination coefficient R 2 between the prediction data and the experimental data was 0.953. The results showed revealed that the data combined with the ANN method is an effective approach for predicting the threshing performance of the flexible threshing device in rice. Moreover, the sensitivity analysis showed that RS, TC, and SC were crucial factors influencing the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least effect on threshing performance, with an average threshing relative importance of 11.65%. Our findings can be leveraged to optimize the threshing performance of future flexible threshing devices.

Suggested Citation

  • Lan Ma & Fangping Xie & Dawei Liu & Xiushan Wang & Zhanfeng Zhang, 2023. "An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device," Agriculture, MDPI, vol. 13(4), pages 1-15, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:788-:d:1111133
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    References listed on IDEAS

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    1. Maertens, K & De Baerdemaeker, J, 2004. "Design of a virtual combine harvester," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 65(1), pages 49-57.
    2. Naji Mordi Naji Al-Dosary & Abdulwahed Mohamed Aboukarima & Saad Abdulrahman Al-Hamed, 2022. "Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables," Agriculture, MDPI, vol. 12(6), pages 1-24, June.
    3. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
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    Cited by:

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    2. Cheng Shen & Zhong Tang & Maohua Xiao, 2023. "“Eyes”, “Brain”, “Feet” and “Hands” of Efficient Harvesting Machinery," Agriculture, MDPI, vol. 13(10), pages 1-3, September.

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