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 ha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/4/788 |
| _version_ | 1827746324937703424 |
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| author | Lan Ma Fangping Xie Dawei Liu Xiushan Wang Zhanfeng Zhang |
| author_facet | Lan Ma Fangping Xie Dawei Liu Xiushan Wang Zhanfeng Zhang |
| author_sort | Lan Ma |
| collection | DOAJ |
| description | 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<sub>P</sub>), impurity rate of the threshed material (Y<sub>Z</sub>), and loss rate (Y<sub>S</sub>) 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) and the average absolute error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>E</mi></mrow></semantics></math></inline-formula>) 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<sup>2</sup> 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. |
| first_indexed | 2024-03-11T05:20:50Z |
| format | Article |
| id | doaj.art-5d252bb7bb3b4b1c8bba9efd7e138dc5 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T05:20:50Z |
| publishDate | 2023-03-01 |
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| series | Agriculture |
| spelling | doaj.art-5d252bb7bb3b4b1c8bba9efd7e138dc52023-11-17T17:53:27ZengMDPI AGAgriculture2077-04722023-03-0113478810.3390/agriculture13040788An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing DeviceLan Ma0Fangping Xie1Dawei Liu2Xiushan Wang3Zhanfeng Zhang4College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, ChinaCollege of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, ChinaCollege of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, ChinaCollege of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410102, ChinaChangsha Zichen Technology Development Co., Ltd., Changsha 410221, ChinaRice 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<sub>P</sub>), impurity rate of the threshed material (Y<sub>Z</sub>), and loss rate (Y<sub>S</sub>) 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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) and the average absolute error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>A</mi><mi>E</mi></mrow></semantics></math></inline-formula>) 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<sup>2</sup> 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.https://www.mdpi.com/2077-0472/13/4/788riceflexible threshing cylinderartificial neural networkthreshing clearance of concave sieveseparating clearance of concave sievefeeding quantity |
| spellingShingle | Lan Ma Fangping Xie Dawei Liu Xiushan Wang Zhanfeng Zhang An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device Agriculture rice flexible threshing cylinder artificial neural network threshing clearance of concave sieve separating clearance of concave sieve feeding quantity |
| title | An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device |
| title_full | An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device |
| title_fullStr | An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device |
| title_full_unstemmed | An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device |
| title_short | An Application of Artificial Neural Network for Predicting Threshing Performance in a Flexible Threshing Device |
| title_sort | application of artificial neural network for predicting threshing performance in a flexible threshing device |
| topic | rice flexible threshing cylinder artificial neural network threshing clearance of concave sieve separating clearance of concave sieve feeding quantity |
| url | https://www.mdpi.com/2077-0472/13/4/788 |
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