Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network
This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal soluti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3682 |
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| author | Yilong Pan Yaxin Yu Junwei Zhou Wenbing Qin Qiang Wang Yinghao Wang |
| author_facet | Yilong Pan Yaxin Yu Junwei Zhou Wenbing Qin Qiang Wang Yinghao Wang |
| author_sort | Yilong Pan |
| collection | DOAJ |
| description | This innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device. |
| format | Article |
| id | doaj-art-a162b0e02b3d4ab3af28d2d253968e67 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a162b0e02b3d4ab3af28d2d253968e672025-08-20T03:08:44ZengMDPI AGApplied Sciences2076-34172025-03-01157368210.3390/app15073682Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural NetworkYilong Pan0Yaxin Yu1Junwei Zhou2Wenbing Qin3Qiang Wang4Yinghao Wang5School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaThis innovative method improves the inefficient optimization of the parameters of a pneumatic drum seed metering device. The method applies a backpropagation neural network (BPNN) to establish a predictive model and multi-objective particle swarm optimization (MOPSO) to search for the optimal solution. Six types of small vegetable seeds were selected to conduct orthogonal experiments of seeding performance. The results were used to build a dataset for building a BPNN predictive model according to the inputs of the physical properties of the seed (thousand-grain weight, kernel density, sphericity, and geometric mean diameter) and the parameters of the device (vacuum pressure, drum rotational speed, and suction hole diameter). From this, the model output the seeding performance indices (the missing and reseeding indexes). The MOPSO algorithm uses the BPNN predictive model as a fitness function to search for the optimal solution for three types of seeds, and the optimized results were verified through bench experiments. The results show that the predicted qualified indices for tomato, pepper, and bok choi seeds are 85.50%, 85.52%, and 84.87%, respectively. All the absolute errors between the predicted and experimental results are less than 3%, indicating that the results are reliable and meet the requirements for efficient parameter optimization of a seed metering device.https://www.mdpi.com/2076-3417/15/7/3682pneumatic drum seed metering devicebackpropagation neural networkmulti-objective particle swarm optimizationparameter optimizationphysical property |
| spellingShingle | Yilong Pan Yaxin Yu Junwei Zhou Wenbing Qin Qiang Wang Yinghao Wang Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network Applied Sciences pneumatic drum seed metering device backpropagation neural network multi-objective particle swarm optimization parameter optimization physical property |
| title | Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network |
| title_full | Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network |
| title_fullStr | Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network |
| title_full_unstemmed | Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network |
| title_short | Research on Performance Predictive Model and Parameter Optimization of Pneumatic Drum Seed Metering Device Based on Backpropagation Neural Network |
| title_sort | research on performance predictive model and parameter optimization of pneumatic drum seed metering device based on backpropagation neural network |
| topic | pneumatic drum seed metering device backpropagation neural network multi-objective particle swarm optimization parameter optimization physical property |
| url | https://www.mdpi.com/2076-3417/15/7/3682 |
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