Intelligent Storage Data Classification System Based on the BP Neural Network
In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and opt...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5771148 |
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| _version_ | 1849683082839326720 |
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| author | Minghui Li |
| author_facet | Minghui Li |
| author_sort | Minghui Li |
| collection | DOAJ |
| description | In order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests. |
| format | Article |
| id | doaj-art-c9be0266a90a41cb9296054b044257e9 |
| institution | DOAJ |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c9be0266a90a41cb9296054b044257e92025-08-20T03:23:59ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/5771148Intelligent Storage Data Classification System Based on the BP Neural NetworkMinghui Li0Henan College of TransportationIn order to solve the problem of multifeature recognition and classification of many kinds of pests, this study puts forward a method of pest feature classification using the BP neural network. Through the preprocessing of stored grain pest images, five characteristic parameters are obtained and optimized and input into the BP network for training. The experimental results show that sample 3 of flat grain thief and sample 4 of bark beetle are not well recognized. Because these two kinds of pests have small bodies and thin legs, some detailed features are eliminated after image processing, resulting in a low recognition rate. But the overall recognition rate can reach 95%. Conclusion. The experiment has obtained good recognition results. This method is accurate and effective for the classification and recognition of stored grain pests and provides a scientific basis for the scientific decision-making of controlling stored grain pests.http://dx.doi.org/10.1155/2022/5771148 |
| spellingShingle | Minghui Li Intelligent Storage Data Classification System Based on the BP Neural Network Journal of Control Science and Engineering |
| title | Intelligent Storage Data Classification System Based on the BP Neural Network |
| title_full | Intelligent Storage Data Classification System Based on the BP Neural Network |
| title_fullStr | Intelligent Storage Data Classification System Based on the BP Neural Network |
| title_full_unstemmed | Intelligent Storage Data Classification System Based on the BP Neural Network |
| title_short | Intelligent Storage Data Classification System Based on the BP Neural Network |
| title_sort | intelligent storage data classification system based on the bp neural network |
| url | http://dx.doi.org/10.1155/2022/5771148 |
| work_keys_str_mv | AT minghuili intelligentstoragedataclassificationsystembasedonthebpneuralnetwork |