Research on Classification Method of Maize Seed Defect Based on Machine Vision
Joint Authors
Sun, Lei
Suo, Xuesong
Huang, Sheng
Fan, Xiaofei
Shen, Yanlu
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-25
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture.
This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection.
In recent years, deep learning has performed well in the field of image recognition.
We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms.
Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs.
79.2% (SURF+SVM).
We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases.
We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results.
As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.
American Psychological Association (APA)
Huang, Sheng& Fan, Xiaofei& Sun, Lei& Shen, Yanlu& Suo, Xuesong. 2019. Research on Classification Method of Maize Seed Defect Based on Machine Vision. Journal of Sensors،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1187326
Modern Language Association (MLA)
Huang, Sheng…[et al.]. Research on Classification Method of Maize Seed Defect Based on Machine Vision. Journal of Sensors No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1187326
American Medical Association (AMA)
Huang, Sheng& Fan, Xiaofei& Sun, Lei& Shen, Yanlu& Suo, Xuesong. Research on Classification Method of Maize Seed Defect Based on Machine Vision. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1187326
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187326