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

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.

Automated summary

In this study, an artificial neural network model was used to predict the threshing performance of a flexible threshing device, and the influence of various factors on the output was explored. The results showed that the rotation speed, threshing clearance, and separation clearance were crucial factors influencing the threshing performance, while the feeding quantity had minimal effect.