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

文献类型： 外文期刊

作者： Ma, Lan 1 ; Xie, Fangping 1 ; Liu, Dawei 1 ; Wang, Xiushan 1 ; Zhang, Zhanfeng 4 ;

作者机构： 1.Hunan Agr Univ, Coll Mech & Elect Engn, Changsha 410102, Peoples R China

2.Chinese Acad Agr Sci, Inst Bast Fiber Crops, Changsha 410205, Peoples R China

3.Hunan Key Lab Intelligent Agr Machinery Corp, Changsha 410102, Peoples R China

4.Changsha Zichen Technol Dev Co Ltd, Changsha 410221, Peoples R China

关键词： rice; flexible threshing cylinder; artificial neural network; threshing clearance of concave sieve; separating clearance of concave sieve; feeding quantity; threshing performance

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 4 期

页码：

收录情况： SCI

摘要： 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 (RMSE) and the average absolute error (MAE) 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.

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