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

Lan Ma, Fangping Xie (), Dawei Liu, Xiushan Wang and Zhanfeng Zhang
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Lan Ma: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
Fangping Xie: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
Dawei Liu: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
Xiushan Wang: College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, China
Zhanfeng Zhang: Changsha Zichen Technology Development Co., Ltd., Changsha 410221, China

Agriculture, 2023, vol. 13, issue 4, 1-15

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.

Keywords: rice; flexible threshing cylinder; artificial neural network; threshing clearance of concave sieve; separating clearance of concave sieve; feeding quantity; threshing performance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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