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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Published inJournal of control science and engineering Vol. 2022; pp. 1 - 7
Main Author Li, Minghui
Format Journal Article
LanguageEnglish
Published New York Hindawi 24.06.2022
John Wiley & Sons, Inc
Wiley
Subjects
Back propagation networks
Bark
Classification
Data compression
Decision making
Fuzzy sets
Grain storage
Image processing
Morphology
Neural networks
Object recognition
Pattern recognition
Personal computers
Pests
Quality management
Software
Ventilation
China
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Abstract 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.
AbstractList 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.
Author Li, Minghui
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ContentType Journal Article
Copyright Copyright © 2022 Minghui Li.
Copyright © 2022 Minghui Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms Back propagation networks
Bark
Classification
Data compression
Decision making
Fuzzy sets
Grain storage
Image processing
Morphology
Neural networks
Object recognition
Pattern recognition
Personal computers
Pests
Quality management
Software
Ventilation
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Title Intelligent Storage Data Classification System Based on the BP Neural Network
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